Browsing by Author "Ayele, Binyam"

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    Application of Data Minig Technology to Identifay Risk Factors of Abortion Incidence and To Identify Their Association Rules: The Case of Marie Stops International Ethiopia Centers
    (Addis Ababa University, 2013-01) Ayele, Binyam; Jemaneh, Getachew (PhD)
    Background: In order to fill the gap in evidence based information, and help in programming for the reduction of maternal deaths due to unsafe abortion, Healthcare industry today generates huge amounts of complex data about patients, hospitals resources, disease diagnosis, electronic patient records, and medical devices. This large amount of data is a key resource to be analyzed and processed to extract hidden information and knowledge. Decision making process at the health care setting needs to be supported with more advanced technology including a computer based information system. Objective: This thesis intends to investigate the potential applicability of data mining technology to identify the major factors that result in abortion and to find their association Methods: A Hybrid Data Mining methodology is followed, which is a six-step knowledge discovery process. The data for this research obtained from MSIE in Addis Abeba, Ethiopia. The experiments carried out in this research using association mining algorithm apriori. On MSIE abortion report datasets, descriptive data summarization was 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. On the basis of subjective (opinions of domain experts) and objective (support and confidence) measures of interestingness, a number of rules having practical relevance or that can add to the current knowledge in the problem domain were identified. Results: The results from this study were encouraging, which strengthened the hypothesis that interesting patterns can be generated from MSIE abortion case database by applying one of the data mining techniques: association rule mining. Besides, the results were promising and encouraging especially in the eye of domain experts. Conclusion: The result thus obtained in this study is promising to apply data mining for identifying the risk factors of induced abortion and prevention. To make usable the knowledge extracted in this study, an attempt has made by selecting best association rules. Keywords: Key words: Data mining, Induced abortion, knowledge discovery, association rule, apriori algorithm.
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    What Factors Determine Delivery Practices of Pregnant Women?
    (Addis Abeba Universty, 2005-05) Ayele, Binyam; Berhane, Yemane (PhD)
    Background:- Ensuring a skilled delivery attendant at each child birth is the most critical intervention in reducing maternal and neonatal mortality and morbidity. In Ethiopia the proportions of births attended by skilled personnel is very low and even for women who have access to the services. The proportion of birth occurring in health facilities is very low. Objective:- This study aims at identifying the factors that influence intention of pregnant women for their delivery and the factors that determine actual delivery service utilizations. Methods:- A prospective community based follow up study is conducted in Jimma town. Women in their child trimester were identified by a complete household survey. Subsequently relevant data were collected in two separated household survey. Results:- A total of 207 women were included in the study. Maternal age and educational status are significantly associated with place of delivery. Woman between 35-39 are less likely to deliver in HFs when compared to those between 15-39 ( OR = 0.06 and 95% CI=0.01 to 0.52) and those with formal education are more likely to deliver in HFs(OR=2.82 and 95%CI1.09 to 7.23). Women who attended ANC follow up ( OR = 2.63 and 95%CI= 1.11 to 6.23) women to whom their husbands and relatives prefer SDA ( OR = 4.78 and 95% CI=2.25 to 10.21) and those who can decide by themselves about getting ID services ( OR = 2.75 and 95 % CI = 1.3 to 5.78) are more likely to deliver in HFs. vii Conclusions:- Empowering disadvantaged women. Promoting ANC follow up and improving the information provision during the follow up. Interventions targeted at husbands and other relatives to direct their influence in favorable fashion and reaching out more women to provide relevant information about ID services are recommended