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
| dc.contributor.advisor | Teferi, Dereje (Associate Professor) | |
| dc.contributor.author | Dileba, Temesgen | |
| dc.date.accessioned | 2018-12-04T07:51:17Z | |
| dc.date.accessioned | 2023-11-29T04:57:01Z | |
| dc.date.available | 2018-12-04T07:51:17Z | |
| dc.date.available | 2023-11-29T04:57:01Z | |
| dc.date.issued | 2012-06 | |
| dc.description.abstract | 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. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/14795 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Health care system is potential area | en_US |
| dc.title | 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 | en_US |
| dc.type | Thesis | en_US |