Health Informatics

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    Design and Development of Drug Scheduling System for Amanuel Mental Specialized Hospital
    (Addis Ababa University, 2017-01-27) Boko, Taditi; Teferi, Derejje (PhD); Deyessa, Nigussie (PhD)
    Introduction The application of Information Technology such as computerized physician order entry, automated dispensing cabinets, bedside bar-coded medication administration, and electronic medication reconciliation are important in avoiding medication errors and enhance quality of care. Electronic medication management includes common hospital process such as prescription of medication, dispensing medication orders, and administration of medications. Electronic medication management can reduce medication errors and can also improve efficiency in the medication management process, such as reducing the time required to locate paper medication charts. Objective The objective of this project is to design and implement Drug scheduling system for Amanuel mental specialized hospital Methodology The methodology used to develop the system is Object oriented analysis and design methodology. Requirement was collected using tools such as interview, observation and document review. Analysis and design of the proposed system was conducted using tools like the data process model, uses case diagram, and class diagram Discussion and Result prescription of antipsychotic medication for psychiatric inpatients is not performed with consistent dosage and duration. As the Medical Doctor who prescribes drug for patients told me, knowing the right dosage of medication is difficult. Estimating the range of dosage depends on the current status of the patient and patient’s response to the medication. For example patient can take 5mg morning and 10 mg evening at 12 hours interval. Using the new system to schedule drug for long duration and inconsistent dosage is difficult. Because of these, when there is change in medication, dosage and duration the system can help the user to edit and update this information. Conclusion Administering medication safely is the most crucial part of patient care. Nurse plays a vital role in administering medication. This activity is performed multiple times in a day for an individual patient. It is a complex process therefore accurate documentation of the medication administration is very important.
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    Designing an Integrated Vital Event Registration System in the Case of Federal Vital Event Registration Agency
    (Addis Ababa University, 2016-06-01) Mekonnen, Abdi; Ayalew, Elizabeth (PhD); Mekonnen, Wubegzier (PhD)
    Most people in Africa and Asia are born and die without leaving a trace in any legal record or official statistics. The inequalities in registration rates are large; developing countries account for 99% of the estimated 48 million unregistered births, with South Asia and sub-Saharan Africa together accounting for 79% of all unregistered births. Ethiopia is among the countries that have not installed national as well as regional civil registration and vital statistics systems. The overall objective of the proposed project is to design an integrated Vital Event Registration System (IVERS) for the Federal Vital Event Registration Agency so as to register, birth, marriage, divorce, notification, death and causes of death and certification at national level. This study employed Object Oriented methodology and qualitative cross sectional case study design methods. User requirement gathering were made through in depth interview and document analysis as major techniques to capture the core business process of the existing manual system. Accordingly, Unified Modeling Language was applied to specify, visualize, construct and document the architecture of a software system. The designed civil registration and vital statistics system registers all births marriage, divorce and deaths, issues birth and death certificates, notifies the authority birth and death from facilities/communities and compiles and disseminates vital statistics, including cause of death information. The proposed project will have significant impact on the healthcare system, policy makers, legal and administrative users as it is intended to systematically lay a foundation for the implementation of a Civil Registration and Vital statistics system that enables access to easy and much flexible vital event information.
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    Predicting Under Nutrition Status of Under-Five Children Using Data Mining Techniques: The Case of 2011 Ethiopian Demographic and Health Survey
    (Addis Ababa University, 2013-06) Markos, Zenebe; Yifiru, Martha (PhD)
    Background: under nutrition is one of the leading causes of morbidity and mortality in children under the age of five in most developing countries including Ethiopia. Objective: The general objective of this study was to design a model that predicts the nutritional status of under-five children using data mining techniques. Methodology: This study followed hybrid methodology of Knowledge Discovery Process to achieve the goal of building predictive model using data mining techniques and used secondary data from 2011 Ethiopia Demographic and Health Survey dataset. Hybrid process model was selected since it combines best features of Cross-Industry Standard Process for Data Mining and Knowledge Discovery in Database methodology to identify and describe several explicit feedback loops which are helpful in attaining the research objectives. WEKA 3.6.8 data mining tools and techniques such as J48 decision tree, Naïve Bayes and PART rule induction classifiers were utilized as means to address the research problem. Result: In this particular study, the predictive model developed using PART pruned rule induction found to be best performing having 92.6% of accurate results and 97.8% WROC area. Promising result has been achieved from the rules regarding nutritional status prediction. Conclusion: The results from this study were encouraging and confirmed that applying data mining techniques could indeed support a predictive model building task that predicts nutritional status of under-five children in Ethiopia. In the future, integrating large demographic and health survey dataset and clinical dataset, employing other classification algorithms, tools and techniques could yield better results. Keywords: Predictive modeling, Nutritional status, children, Data mining, EDHS dataset
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    Analyzing the Outbreak Surveillance and Response System in Ethiopia using Data Mining Techniques
    (Addis Ababa University, 2012-11) Mohammed Yimer; Abebe, Ermias (PhD); Addisse Adamu (PhD)
    The aim of this research work was to show the applicability of data mining techniques for the development of descriptive and predictive model to disease outbreak surveillance datasets in Ethiopia. To do that the three data mining applications such as classification, clustering and association rules mining were undertaken to explore the important applications to the datasets of the PHEM sectors from different perspectives. A total of 18600 records were collected and assessed from the data store of the surveillance system from the year 2004-2012G.C. After the preprocessing phase of knowledge discovery in databases of data mining application a total of 8796 records were prepared for data mining algorithms. From the total records filtered and prepared for model preparation 4703 were from the IDSR system dataset and the remaining 4093 records were taken from that of the PHEM dataset from the year 2004- 2008G.C. and 2009-2012G.C. respectively. The researcher analyzed two classification algorithms for the prediction of Epidemic typhus disease cases with decision tree J48 classifiers and Naïve Bayes classifiers. Finally the more performing algorithm has been taken for model development. From the results of the experiments done decision tree algorithm had a better performance to classify the disease cases in place and time setting. The accuracy rate of correctly classifying the Epidemic Typhus disease cases by the use of decision tree J48 algorithm was 87.44% whereas with Naïve Bayes classifier was 83.70%. The sensitivity and specificity test was also done for the two classifiers. The researcher also attempted to analyze the application of association rule mining to find some sort of correlation or patters among disease cases of the surveillance data. The attributes were selected only from the disease cases for the occurrence and nonoccurrence, which were collected in time and place bases. Here, Apriori association rule mining algorithm was run to find interesting patterns among the occurrence and co-occurrence of disease cases which were correlated together. The researcher used 20% for the minimum support and 90% for minimum confidence threshold before the application of the mining algorithm. The researcher took the combined (integrated) datasets for cluster analysis with the total numbers of 8796 records with 9 attributes. Simple K-Means clustering algorithm was used for the combined datasets since; the algorithm showed the grouping of disease cases with respect to time and place. In general data mining techniques were important and applicable in the classification, clustering and association rules model development for emerging and reemerging disease cases. But the datahas to have good quality with the inclusion of important attributes of variables for better prediction and description model development The results of the research, apart from its education purpose, were also used for the planning, preparedness, decision making, and disease control and prevention activities to the domain experts.
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    Assessment of Client-Health Care Provider Communication at Antenatal Care Clinics in Addis Ababa, Ethiopia
    (Addis Ababa University, 2012-06) Tadesse, Worku; Shiferaw, Solomon (PhD); Lamnew, Worksheet (PhD)
    Background Health care provider-client communication during Antenatal care is an effective strategy to improve maternal health care seeking behavior and satisfaction with health services. However, the presence of miscommunication, lack of communication, or unsatisfactory communication between health care providers and clients pose a significant challenge in the health care service utilization. Objective The aim of this study was to assess the client-health care provider communication including client satisfaction in public hospitals at antenatal care clinics, in Addis Ababa, Ethiopia. Method: A cross-sectional study design using both quantitative and qualitative methods was employed from October to June 2012. 425 consecutive clients of antenatal care took part in the study. Additionally, in-depth interview was conducted among 15 purposively selected antenatal clients, 17 conveniently selected health care providers using interview guides. Results: The mean (SD) age of antenatal clients was 27.6±4.7 years; majority having secondary (33.4%) and tertiary education (34.4%); and Only 161(37.9%) antenatal clients were satisfied by health care providers‟ quality of communication. Getting care by one provider at different visits (AOR=0.55;95%CI:0.32,0.96), longer duration of time for discussion(AOR=0.29;95%CI:0.11,0.77), clients‟ feeling of privacy(AOR=0.47;95%CI:0.22,0.99),health care providers‟ empathy (AOR=0.41; 95%CI:0.22,0.77) and information provision (AOR=0.09; 95% CI: 0.05, 0.17) were significantly associated with better client satisfaction. Time constraint due to heavy clients‟ load (88.0%), multiple clinical task (71.0%), and lack of dedicated space for communication (58.8%) were the most common barriers of optimal communication reported by health care providers. Conclusion and Recommendation: more than 3 out of 5 of antenatal clients were not satisfied by the providers‟ quality of communication. Insufficient discussion time, suboptimal health care providers‟ empathy and information provision, lack of feeling of privacy and lack of continuity of care by same health care providers were the main factors that contributed to the low antenatal client satisfaction on health care provider‟s quality of communication. Better demonstration of empathy, information provision, longer discussion time, continuity of care with one provider and providing sufficient feeling of privacy should be encouraged to improve antenatal clients‟ satisfaction on health care providers‟ quality of communication. Attempt should be made to free health care providers at ANC from multiple clinical tasks with more attention given to ensuring dedicated space to improve optimal provider-client communication.
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    Architectural Framework for Information Integration: Case of Organizations Working on Water, Hygiene and Sanitation in Ethiopia
    (Addis Ababa University, 2013-05) Mulualem, Wondwossen; Enquselassie, Fikrie (PhD)
    Water, Hygiene and Sanitation (WASH) is a subject of intersectoral interest that engages different governmental and non-governmental organizations. As it happens in the other areas of public health, existence of parallel WASH activities makes the sector prone to the problems of fragmentation, lack of consistency and other problems that plagued the sector. Such problems were felt at the national level by the Ethiopian government and, as the result, movement towards integrating all WASH activities in the nation was started by drafting a WASH implementation Framework (WIF) and Memorandum of Understanding which eventually was signed by four governmental organizations. The WIF aims at integrating all aspects of WASH activities, including the information system, under one umbrella and the interest of this research lies on exploring the possibility for creating a framework for an integrated WASH information system that can be shared by all stakeholders. The main objective of this research was to study the current status of WASH data creation, management and sharing practices among organizations working in the sector and propose an architectural framework that can be considered as a guide to setup an Integrated WASH information system. To this end, this research focused on the use of indicators as primary tools for data integration and attempted to study types of WASH activities performed and indicators used by target organizations, types of data they collect under each indicator, formats for data collection including the data attributes being used, standards being used for formulation of indicators and practice of sharing between organization working in the sector. Relevant data was collected mainly through semi-structured interviews and analysis of relevant documentations provided by the respondents. The result was eventually used to propose an architectural framework that can be considered as a starting point for practitioners working in the area. The framework was discussed with selected respondents for checking its validity and the overall reaction of the respondents was found to be positive. Keywords: Health Information Architecture, Health Information Architectural Framework, Water, Hygiene and Sanitation information System, Water, hygiene and Sanitation information Integration, Public Health Information System, Indicator-data Linkage. Indicator definition, Indicator standardization, standardized data definition
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    Data Exchnage Interoprability Framework for Laboratory Information System (Lis) and Electronic Health Record (Ehr) of Two Hospitals in Addis Ababa
    (Addis Ababa University, 2013-06) Shiferaw, Wondwosen; Enquselassie, Fikre (PhD)
    Background: eHealth applications are key to facilitate the health care delivery by increasing access and quality. However, those applications are vary in design and structure. Interoperability between those applications is important that creates a bridge and facilitates the data exchange between two different eHealth applications. In Ethiopian hospitals, eHealth applications are characterized by small-scale in size with greater duplication of efforts. For instance, one of the gap has been the integration of applications such as Laboratory information system (LIS) and Electronic Health Record (EHR) due to limited interoperability in laboratory data exchange between these applications in the Hospitals. Objectives: The objective of this study is to assess the existing LIS and EHR for data exchange interoperability and identify challenges with the view to explore the possibility of proposing and developing data exchange interoperability framework. Methodology: In this study, an attempt is made to apply qualitative research method to explore the status of e-Health applications (LIS and EHR). Interviews were used tool for data collection. We have also collect the criteria from the literature on LIS and EHR application functionality testing. Finally, then constructive method helped for design data exchange interoperability framework on eHealth applications in the case of LIS and EHR. We used NVivo-10 software for coding the interview and analyze the data. Results: In this study, lacks of interoperability and coordination cause for fragmentation of systems and data redundancy on eHealth applications which are the main challenges in the Hospitals. In order to overcome the data exchange interoperability problems in the Hospitals, the researcher suggested data exchange interoperability framework between LIS and EHR. The framework is tested by the developers and decision makers. The proposed and designed framework is encouraging for future change. Conclusion: the study was designed to explore the current status of eHealth applications in the selected case. The investigator shown that there have been challenges in terms of data exchange interoperability, integration and coordination of the systems. The proposed data exchange interoperability framework is a possible solution to address the challenges
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    Assessment of the Feasbility of Using Text Messages Among Art Follow-Up Patients to Improve Drug Adherence in Selected Art Units in Addis Ababa City Administration. Addis Ababa
    (Addis Ababa University, 2010-06) Hailu Tsega; Fantahun Mesganaw (Professor)
    Back ground: PLWHA on ART follow –up individuals may or may not have mobile phones, ability to use text messages and willingness to receive text message reminders to take their drugs .But the extent of theses desires and how it varies by individual, social, health and demographic characteristics is not well understood. Objective: to assess the feasibility of using text messages among ART follow up patients to improve drug adherence in selected ART units in Addis Ababa city administration Method: The study was undertaken from March to April 2010, using quantitative cross-sectional study supplemented by qualitative in-depth interview on a sample of 461 PLWHA on ART follow up care for quantitative and 14 respondents for qualitative. Study subjects were selected using stratified random sampling method. A pre- tested structured questionnaire was used to collect data; Data were entered, cleaned, and analyzed using SPSS version 16 Result- One hundred twenty five (82.8%) male and ninety-three (79.5%) female over all 218 (81.3 %) of the total respondents PLWHA on ART follow-up in Addis Ababa were willing to receive (SMS) text message reminders. Of those who have willingness to receive text message reminders had disclosure of HIV status to their partner or family with (adjusted OR: 0.03, 95%CI :( 0.01-0.08) times more likely willing to receive SMS text message reminders than those who did not disclose their HIV status. In addition, respondents who attended elementary school (adjusted OR: 8.21, 95% CI: 1.59-42.33) times more likely to receive text message than those who do not attended school and those who secondary school (adjusted OR: 58.65, 95% CI: 12.18-280.12) times more likely to receive text messages than those who did not attended school. One hundred forty eight (68.2%) of PLWHA on ART follow-up individuals wanted to receive text message reminders of time to take their drugs Conclusion: High proportion of HIV positive individuals on ART follow-up wanted to receive text message reminders, of time to take their drugs. Their willingness to receive text message reminders of these people has implication to introduce or adoption (SMS) text message technology with designed special computer software program that automatically sends special message service (SMS) to improve drug adherence.
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    Assessment of Knowledge Sharing Practices of Health Care Professionals in Hospitals Under Addis Ababa Health Bureau
    (Addis Ababa University, 2011-06) Yalew Tirualem; Addisse Mesfin (PhD)
    Background: - Knowledge is the most important strategic resource in organizations, and its management is critical to organizational success. Knowledge sharing is a social interaction culture, involving the exchange of employee knowledge, experiences, and skills through organization. However there is no previous study that assesses the knowledge sharing practice in AA. Therefore the purpose of this study is to assess the knowledge-sharing practices and identify factors that affect knowledge sharing practices among health professionals. Objective: -To investigate the level and factors associated with knowledge-sharing practice among health care professionals in public hospitals of Addis Ababa. Methods:-A cross-sectional study with both quantitative and qualitative methods was conducted among 5 hospitals under Addis Ababa Health Bureau from May to June 2011 G.C. Total of 318 respondents were selected using simple random sampling technique. The data were collected using self administered structured questionnaire and to supplement the quantitative study in-depth interviews were also conducted. The data were entered and cleaned using Epinfo version 3.5.1 and analyzed using SPSS version 16. Frequencies and percentages were used to describe the study population and Logistic regression with 95% confidence interval was used to assess the presence and degree of association between dependent and independent variables. Result: - The study revealed that 50.3% of the respondents were engaged in active knowledge sharing practice. More than half (57.2%) of the respondents were not satisfied with their job, and 69% of participants report the absence of motivational schema in their health institutes. Over all 72% of respondents are willing to share their knowledge. The factors that were independent predictors of knowledge sharing were job satisfaction, very high level of motivation, extrinsic motivation, use of communication channel and the presence of knowledge sharing opportunity. Those respondents who were satisfied with their job were more likely to share their knowledge than the others with the odds ratio, AOR[95%CI] 1.73[1.00-2.98], who had high level of motivation were more likely to share their knowledge than the others with the odds ratio, AOR[95%CI] 3.38[1.04-11.00], and those respondents who were extrinsically motivated were more likely to share their knowledge than the others with the odds ratio, AOR[95%CI] 1.75[1.02-2.99].The respondents who used communication channels were more likely to share their knowledge than the others with the odds ratio , AOR[95%CI] 3.05[1.71-5.45] and who had knowledge sharing opportunity were more likely to share their knowledge than the others with the odds ratio, AOR[95%CI] 2.89[1.70-4.90]. Conclusion and Recommendation: - From this study most of the respondents were aware of the importance of knowledge sharing but only half of respondents were engaged on active knowledge sharing practice, and the factors that were independent predictors of knowledge sharing were job satisfaction, high level of motivation, extrinsic motivation, use of communication channel, the presence of knowledge sharing opportunity. So stake holders (AAHB, the hospitals) should device a way for strengthen knowledge sharing practice through improving all the hinderers of knowledge sharing.
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    Assessment of the New Health Management Information System Implementation in Public Health Facilities and Institutions in Addis Ababa
    (Addis Ababa University, 2010-06) Alemu Tilahun; Addisse Mesfin (PhD)
    Background: Health Management Information System (HMIS) supports informed strategic decision making through the production of quality data and information for action that helps mangers and health workers plan and manage the health service system. HMIS is one of the major ’core’ activities given due attention even though the ongoing ‘businessprocess re-engineering’ in HMIS program has resulted several measures to be taken. Objective: To assess the new HMIS implementation status in Addis Ababa. Methods: A cross–sectional, descriptive study was conducted. Purposive sampling was used. Structured questionnaire, in-depth interview and observation were made between March and April 2010. Both HMN and PRISM frameworks were used to evaluate the results. Result: There were 220 respondents and among them, 80% were clinician health workers. The majority of respondents (94.5%) did not participate in the designing efforts of the new HMIS and one hundred fifty one (68.6%) respondents are currently involving in the HMIS activities. There is no incentive (93.6%).Two hundred and eleven (95.9%) respondents use HMIS for reporting purpose. Implementation lacks ownership (91.4%), coordination and leadership (85%), strategy and policy (63.3%), motivation (92.7%), shared responsibility among stack holders (57.7%) and not considered as the extension of the previous HIS(92.%). Conclusion: large numbers of respondents currently participate in the manual based HMIS activities through a well designed data collection and reporting formats. A short period on job-training, absence of incentive, motivation and lack of management support lead to poor information use culture that is limited mainly for a send-report purpose. HMIS Implementation suffered from ownership, follow-up, communication and leadership. Recommendation: HMIS should be a core activity furnished with appropriate human, material and financial resources. Improved data processing and management should be accompanied by Skilled and trained health workers with appropriate ICT use. Management shall support, facilitate and motivate information use culture. HMIS implementation in Addis Ababa should have ownership, follow up, coordination, cooperation and communication among stack holders. Key words: HMIS, HIS, Data Quality, Information Use, Implementation status.
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    Mining Echocardiography Data to Predict Heart Disease (Medphd)
    (Addis Ababa University, 2012-06) Habte, Thomas; Meshesha, Million (PhD)
    Background: These days, a major challenge of health care is reaching to correct diagnosis of specific disease condition. Poor clinical decision leads to catastrophic consequences which are unacceptable. Decision making process at the health care setting needs to be supported with more advanced technology including a computer based information system. Objective: This study aims at extracting hidden knowledge (patterns and relationships) associated with echocardiography datasets and designing a predictive model for heart disease detection using data mining techniques. Methods: A Hybrid Data Mining methodology is followed, which is a six-step knowledge discovery process. The data for this research obtained from International Cardiovascular Hospital in Addis Abeba, Ethiopia. This study investigates the use of different data mining techniques, Decision tree, neural network, and support vector machine for classification tasks. On Transthoracic Echocardiography report datasets, descriptive data summarization and visualization were taken to gain understanding of the data. Moreover, missing values, outliers data, data integration and transformation were managed at preprocess stage of hybrid process model. Results: The results show that all the models performed well, though J48 Decision tree algorithms outperforms support vector machine, Multilayer Perceptron Neural Network, registering 96.73%. The best attributes selected by J48 decision tree are Left Atrium Systole Diameter, LV ejection fraction, and Tricuspid velocity. As per discussion made with the cardiologist, one of the interesting rule, a patient with Left atrium systole diameter less than or equal to 40 millimeter and LV ejection fraction less than or equal to 51% blood pumped out of ventricles and Tricuspid velocity is greater than 2.5 centimeter per second results Left Ventricle dysfunction and Pulmonary hypertensive disorder. Conclusion: The result thus obtained in this study is promising to apply data mining for heart disease detection. To make usable the knowledge extracted in this study, an attempt has made to design a knowledge-based system that shows the potential to integration. It is a further research direction.Keywords: Echocardiography, Knowledge discovery process, Decision tree, neural network, Support vector machine
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    Application of Data Mining For Predicting Adult Mortality
    (Addis Ababa University, 2012-06) Hailemariam, Tesfahun; Meshesha, Million (PhD)
    Background: The fast-growing, tremendous amount of data, collected and stored in large and massive data repositories, has far exceeded human ability for comprehension without powerful tools. As a result, data collected in large data repositories become seldom visited. This in turn, calls the application of data mining technology. Every year, more than 7·7 million children die before their fifth birthday. However, over three times those of nearly 24 million adults die every year. Less attention has been given to adults which are the most productive phase of life for both economic and social ramification of families and countries. Objective: The general objective of this research is to construct adult mortality predictive model using data mining techniques so as to identify and improve adult health status using BRHP open cohort database. Methods: The hybrid model that was developed for academic research was followed. Dataset is preprocessed for missing values, outliers and data transformation. Decision tree and Naïve Bayes algorithms were employed to build the predictive model by using a sample dataset of 62,869 records of both alive and died adults through three experiments and six scenarios. Result: In this study as compared to Bayes, the performance of J48 pruned decision tree reveals that 97.2% of accurate results are possible for developing classification rules that can be used for prediction. If no education in family and the person is living in rural highland and lowland, the probability of experiencing adult death is 98.4% and 97.4% respectively with concomitant attributes in the rule generated. The likely chance of adult to survive in completed primary school, completed secondary school, and further education is (98.9%, 99%, 100%) respectively. Conclusion: The study suggests that education plays a considerable role as a root cause of adult death, followed by outmigration. Further comprehensive and extensive experimentation is needed to substantially describe the loss experiences of adult mortality in Ethiopia. Key words: BRHP data, Mortality, Adult, predictive model, J48 decision tree, Data Mining.
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    Application of Data Mining Techniques to Discover Cause of Under-five Children Admission to Pediatric Ward: The case of Nigist Eleni Mohammed Memorial Zonal Hospital
    (Addis Ababa University, 2012-06) Dileba, Temesgen; Teferi, Dereje (Associate Professor)
    Background: - Health care system is potential area to apply and take the advantage of data mining. Higher priority is given for the prevention and control of preventable disease at home or community level. However, for seriously ill children admissions should be facilitated in order to save the life of the child. Objectives: - The objective of this study is to apply data mining techniques on under five children dataset in developing a model that support the discovery of the causes for under-five children admission to pediatric ward. Methodology: - Cross industry standard process for data mining process model was applied. Major processes covered were business understanding, data understanding, data preprocessing, modeling and evaluation. Decision tree and artificial neural network algorithms were tested for classification tasks in Waikato Environment for Knowledge Analysis. Exploratory data analysis techniques, graphs and tabular formats for visualization and accuracy, true positive rate, false positive rate, receiver operating characteristic and the idea of experts were used for evaluation of the model. The dataset used was records in integrated registration log book in under-five outpatient department. Result: - The decision tree algorithm J48 has higher accuracy (94.77%), weighted true positive rate (94.7%), weighted false positive rate (5.3%), weighted receiver operating characteristics (0.99) and performs much faster than multilayer perceptron. According to interesting rules in J48 presenting complaint of not taking any food, fluid or breast feeding (98.32%), low weight for age without sunken eyes (92.31%) and very low weight for age but not in association with restless or irritable (98.33%) are among the cause of under-five children admission to pediatric ward without any consideration of health information management system admission disease classification. Conclusion: - In conclusion, encouraging results are obtained in classification tasks, data mining technique is applicable on pediatric dataset in developing a model that support the discovery of the causes of under-five children admission to pediatric ward. The outcome of this study serves primarily users in the domain area, decision makers and planners.
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    Application of Data Mining Techniques on Antiretroviral Therapy (Art) Data: The Case of Adama and Asella Hospitals
    (Addis Ababa University, 2010-06) Urgessa, Teklu; Meshesha, Million (PhD)
    Human Immunodeficiency Virus/ Acquired Immunodeficiency Syndrome (HIV/AIDS) is of global as well as national concern today as it affects all people of the world regardless of sex, age, educational status, race and color. When we come to Sub-Saharan African region in general and Ethiopia in particular, the situation is even more worsening and needs special attention. Today more than 1 million people are living with HIV/AIDS in Ethiopia. The country has made a lot of efforts towards preventing and controlling of the disease. As a result, hundreds of thousands of people come to health facilities to get Counseling and testing services through Voluntary Counseling and Testing (VCT) and Antiretroviral Therapy (ART) programs. A lot of demographic and Clinical data is recorded about individuals taking the services. As these data is getting larger and larger, it is highly likely that there will be hidden, implicit and non trivial knowledge within the data, which might not be obtained by the traditional statistical analysis as well as report and query based database functionalities. There are various evidences that Data Mining (DM) helps the health care system to extract non-trivial and hidden knowledge which exists within the large volume of demographic and clinical data captured during the provision of services and that this knowledge is helpful for health administrators to target resources in the right directions for preventive and controlling activities, and clinicians to give safe and right treatment and saves humans’ lives. Therefore; the main objective of this research was to see the applicability of data mining techniques on ART data collected at facility level by taking the case of Adama and Asella Hospitals ART databases to identify important patterns related to determinant attributes and their values for Termination/ Continuity behavior of patient on ART care service. Various data preprocessing activities were made to come up with the dataset ready for model building. The researcher selected two DM functionalities (Classification and Association rules mining). Decision tree classification with J48 implementation with eight scenarios was experimented. Thirteen experiments with different parameters were made for association rule mining. Evalution of the models was performed by using for each DM functionality and scenarios used to model the dataset. Analysis of the model was made based on different criteria mainly using confusion matrix, accuracy measures,time of execution and tree complexity for decision tree classification models and number of rules generated, support and confidence for each scenario of the association rule The research showed encouraging results; that data mining techniques are of high potential in predicting determinant factors/attributes for termination/continuity behavior of ART care by the patients. Finally hidden patterns (knowledge) were extracted that will provide certain decision support information for concerned bodies, for ART programs intervention. To mention few, the result showed for example that those patients who were on ART stage and whose Functional status is bedridden and the year in which they began the service is before 1999 E.C are at high risk of terminating the ART care. Those patients whose ART stage is on ART, and whose functional Status is Ambulatory, and if they started the service before 1999 and their age is above 18 years then they have high chance to terminate the ART care. The study also showed certain hidden information that young people whose age is less than 18 years; have high chance of staying longer in ART care service. Patients terminate the service in shorter time at Asella hospital than at Adama hospital. Those who are jobless have high chance to stay in the care. The reason (s) for these hidden patterns is left open for future researches works. From comparisons done among the experimentations made, it was learned that those data mining techniques, which were experimented for this research are applicable on the ART dataset of the cases under investigation in general but generalized decision tree with pruning outperformed for classification purpose on the dataset in terms preciseness, providing general insight, Performances and accuracy measures with fair execution time and providing best interpretable patterns. Many association rules were obtained with minimum support of 30% and confidence 50% had provided optimum rules with acceptable patterns.
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    Constructing a Predictive Model For Occurrence of Tuberculosis: The Case of Menelik Ii Hospital and St. Peters Tb Specialized Hospital
    (Addis Ababa University, 2013-05) Mulugeta, Teketel; Yifru, Martha (PhD)
    Background: Tuberculosis is a disease of poverty affecting mostly young adults in their most productive years. In Ethiopia, TB is a disease of major public health problem. Early identification and isolation of TB cases is critical to prevent further transmission, morbidity and mortality caused by TB. Data mining has a potential to indentify hidden knowledge from huge datasets. It is possible to use data mining algorithms for analysis and predicting the TB status of patients. Objective: The goal of this research was to apply data mining techniques for predicting the TB status of patients. Specifically, identify the determinant attributes of TB status of patients, build best prediction model and finally develop a prototype graphical user interface. Methodology: A hybrid data mining process model that involved six steps is followed. This study considers a total of 10,031 records from Menelik II and St. Peters TB specialized hospitals patients’ data and 15 attributes for predicting the TB status. Descriptive data analysis, visualization and statistical summary were implemented to gain understanding of the data. Handling of missing values and data transformation were done to prepare the dataset for experimentation. The mining algorithms used are decision tree, naïve bayes, support vector machine and artificial neural network. To evaluate the models performance 10-fold cross validation and confusion matrix are used. Results: The result of the experiments with all and selected attributes showed that performance of J48, Sequential minimal optimization and Multilayer perceptron were better with all attributes than best selected attributes, whereas naïve bayes classifier performance increased with selected attributes than all attributes. The results of the experiments show the performance of mining algorithms decreases as the amount of training increases. The best selected model to predict the TB status of patients in this study was generated by J48 decision tree with all attributes. The accuracy of this model is 95.24%. Graphical user interface prototype was designed using the ten rules from J48 decision tree. Conclusion: The results achieved from this research indicate that data mining is useful in bringing relevant information from large and complex patients’ dataset, and we can use this information for predicting TB status and decision making. The most important attributes that determine the TB status of the patients are shortness of breath, chest pain, cough, weight loss, loss of appetite, night sweats and HIV test results.
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    Developing A Predictive Model For Fertility Preference of Women of Reproductive Age Using Data Mining Techniques
    (Addis Ababa University, 2013-01) Debela, Tariku; Jemaneh, Getachew (PhD); Tamiru, Melesse (PhD)
    Background: Fertility is one of the major factors that determine the overall size, distribution and/or structure of a population. High fertility in developing countries(particularly, in the poorest of those countries) poses detrimental consequences like a high fraction of women experiencing pregnancies of order five and above and a greater likelihood of short inter-pregnancy intervals. These are threat to the health of mothers and their children. At a macro-level, high fertility also contributes to high population growth which in turn results in slow economic growth, environmental degradation and unemployment, among others. Assessing fertility preference helps identify the proportion of women who demand for children and those who intend to limit childbearing. This aids in developing and implementing appropriate intervention programs for the purpose of achieving reductions in fertility levels necessary to slow population growth. Objective: To explore the possibility of applying data mining techniques in developing a model that can predict fertility preferences of women of reproductive age from EDHS2011 women’s survey dataset collect by CSA. Methodology: For this study, a six-step hybrid knowledge discovery process model was adopted. Through the steps, a dataset containing 15 attributes and 16515 records of women was constructed for building models. Results: Three data mining classification algorithms, J48, Naïve Byes and neural Network (Multilayer Perceptron), were tested using 10-fold-cross-validation. The classifiers were implemented on the dataset with all and selected features. Several experiments were constructed and the accuracy achieved on selected feature subset was 75.92%, 77.34%, 78.03% for Naïve Bayes, Multilayer Perceptron and J48, respectively. Conclusion: In this study, feature selection generally improved prediction performance of the classifiers. J48 model with accuracy of 78.03% was found to be relatively better predictor of fertility preference of women. This research study did indicate that data mining can be applied to women’s dataset to identify determinants of fertility preference and classify women according to their childbearing preferences. Age, number of living children, education, child death experience, marital status, sex of child and region are found to be the most important factors that determine fertility preference ofwomen.
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    Success Factors for Implementation of Enterprise Resource Planning System at Ethiopian Airlines
    (Addis Ababa University, 2014-05) Demeke Sintayehu; Lamenew Workshet (PhD)
    This research work finds out the factors that determine the success of an Enterprise Resource Planning (ERP) system implementation with particular reference to Ethiopian Airlines. An Enterprise Resource Planning system is a corporate wide information system which is used to integrate the business processes and resources of a company. When the business environment of a company increases and becomes complex, it is difficult to continue with the traditional decentralized information systems for timely decision making and other activities. In today’s competitive business environment, ERP systems are found to be essential for companies to get competitive and strategic advantages. Developing countries like Ethiopia are not yet benefiting much from ERP systems while these systems are widely adopted in the developed countries. However, demand to ERP is increasing to the developing countries also due to economic growth and globalization. On the other hand, implementation of ERP systems is a huge investment and is not an easy job. Successful ERP systems can boost a company’s efficiency while failed ERP systems can damage its performance. Implementation of ERP system is also affected by different organizational, social, economic, cultural, national and technical factors. So, it is important to identify contextual factors which can lead to successful ERP projects in Ethiopian context since Ethiopian situation is distinctive of developed nations.This research takes the case of SAP ERP system implementation project at Ethiopian Airlines. The research is qualitative type case study which has mainly used interviews, observations and an online survey questionnaire as research tools and techniques. Purposive sampling is used to select interviewees and survey respondents among the project managers, team leaders, super users and project members. Data collected in this study is analyzed by inductive reasoning and triangulation of data from the different sources. The result of this research found out twenty critical success factors for success of ERP systems. Factors such as project planning, top management support, project management and leadership, capability of consultants, change management and communication, organizational readiness and overall knowledge transfer are among the factors found to be critical for ERP system implementation in the Ethiopian context.
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    Designing an Information Extraction System for Amharic Vacancy Announcement Text
    (Addis Ababa University, 2011-06) Hirpassa, Sintayehu; Abebe, Ermias (PhD)
    The number of Amharic documents on the Web is increasing as many newspaper publishers started providing their services electronically. The unavailability of tools for extracting and exploiting the valuable information from Amharic text, which is effective enough to satisfy the users has been a major problem and manually extracting information from a large amount of unstructured text is a very tiresome and time consuming job, this was the main reason which motivate the researcher to engage in this research work. The overall objective of the research was to develop information extraction system for the Amharic vacancy announcement text. The system was developed by using Python and visual basic programming language and rule-based technique was applied to address the problem of automatically deciding the correct candidate texts based on its surrounding context words. 116 Amharic vacancy announcement texts which contain 10,766 words were collected from the ―Ethiopian reporter‖ newspaper published in Amharic twice in week. For this study, nine candidate texts are selected from Amharic vacancy announcement text, these are organization, position, qualification, experience, salary, number of people required, work agreement, deadline and phone number. The experiments have been carried out on each component of a system separately to evaluate its performance on each components, this helps us to identify drawbacks and give some clue for future works. The experimental result shows, an overall F - measure of 71.7% achieved. In order to make the system to be applicable in this domain which is Amharic vacancy announcement, further study is required like incorporating additional rules, improving the speed of the system by modifying the algorithm, a well designed user interface and integrating other NLP facilities.
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    Bilingual Script Identification for Optical Character Recognition of Amharic and English Printed Document
    (Addis Ababa University, 2011-06) Abebe, Sertse; Teferi, Dereje (PhD)
    OCR is a type of document image analysis techniques to recognize the informative content in the text documents to be archived in softcopy for different purposes. The technique involves in conversion of the given image of text to its most probable similar character in a given domain language scripts. A line of a multilingual document page may contain text words in different languages. To recognize, such a document page, it is necessary to identify different script forms before running an individual OCR system. In this paper, a system that distinctly identifies Amharic and English Scripts from a document image is presented. The system addresses the language identification problem on the word level. In extracting the important feature values of word-image of the scripts, preprocessing activities such as noise removal, binarization, segmentation, size and style normalization activities are performed. Maximum Horizontal Projection profiles from three selected region, extent of the word image, and the ratio of the number of connected component to the word-image width are the important feature value to discriminate the two languages script. Support Vector Machine algorithm is applied to classify new instance word images. The proposed algorithm is tested with significant number of words with various font styles and sizes. The results obtained are quite promising and encouraging
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    Mobile Health Application in Ethiopia: Existing Initiatives and Practices
    (Addis Ababa University, 2013-06) Jemberu, Serkalem; Assefa, Demeke (PhD)
    Background: The use of ICT, such as computers, mobile phones, satellite communications, patient monitors, etc., to support health services and information exchange are becoming common place. The mHealth field has emerged as a sub-segment of eHealth. As such, it is observed that mobile technology has the potential to improve the quality, safety, and efficiency of health care services and to impact almost every aspect of the health sector. The realization the potential of mobile devices to improve healthcare system resulted in many initiatives in mobile health in Ethiopia. Those initiatives have showed huge opportunities in the healthcare industry so far; however, whether they are sustainable and scalable remains to be seen. Objective: The main objective of this study is to explore the existing mHealth projects in Ethiopia with regard to their challenges, lessons learned and success factors. Method: The exploratory study design was conducted using qualitative research approach. The purposive sampling research method was adopted to select participants of the study. The data were then collected using in-depth interview, questionnaire and document review. Results: The researcher identified eight mHealth projects (seven are ongoing and one has ended). It is learnt that these projects are facing many challenges. Amongst them are poor network coverage, limited technological literacy of health workers, and sustainable financial support. Developing user-friendly system, pilot assessment and having M&E framework are some lessons taken from some of mHealth projects. Conclusion: Generally, we may conclude that mHealth projects have a significant impact in healthcare services delivery if government commitment, collaboration, capacity building of health workers, financial support, infrastructure and M&E are in place.