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Addis Ababa University Libraries Electronic Thesis and Dissertations: AAU-ETD! >
Faculty of Informatics >
Thesis - Health Informatics >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/3553
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| 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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