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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3553

Title: MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM
Authors: TADESSE, BEYENE
Advisors: Dr. Million Meshesha
Dr. Mitike Molla
Keywords: vital statistics data, Machine Learning, data mining, predictive models, classification, Weka.
Copyright: Jun-2011
Date Added: 6-Aug-2012
Publisher: AAU
Abstract: Data mining is a relatively new field whose major objective is to extract knowledge hidden in large amounts of data. Vital statistics data offer a fertile ground for data mining by providing a valuable source of information regarding the health status of a population. One of the most important public health functions is monitoring of a population’s health status. At all levels of the health delivery structure a well organized health information system is vital for identifying the health needs of populations and for planning, implementation and monitoring of health interventions. The aim of this study is to discover knowledge that can be used to gain insight into various aspects of mortality in the selected rural area of the country. The study explores the death aspect of the vital statistics data in the Butajira Rural Health Program- BRHP database at Butajira, Ethiopia. A data mining tool called Weka is used to build predictive model of 95,220 cases over an eighteen-year period. A historical cohort study analysis of vital statistics is conducted. It follows a JDM process modeling. This study apply classification algorithm, such as to extract interesting knowledge from temporal data on BRHP database. The results obtained in the study contain valuable new information. These results conveyed some interesting findings. The classification algorithm reveals that the result indicates for the BRHP dataset, over 90% accurate results are possible for developing classification rules that can be used in prediction. From this result the researcher concludes that the vital statistics data can help to predict using the application of data mining classification technique given the limitation of this study. In general, the result from this study is encouraging.
Description: A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfilment of the Requirements for the Degree of Master of Science in Health Informatics
URI: http://hdl.handle.net/123456789/3553
Appears in:Thesis - Health Informatics

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