Mining Vital Statistics Data: the Case of Butajira Rural Health Program
| dc.contributor.advisor | Million Meshesha | |
| dc.contributor.author | Tadesse Beyene | |
| dc.date.accessioned | 2025-09-05T21:40:46Z | |
| dc.date.available | 2025-09-05T21:40:46Z | |
| dc.date.issued | 2011-06-01 | |
| dc.description.abstract | Data milling is a relatively new field whose major objective is to ex tract knowledge hidden in large amounts of data. Vital statistics data offer a fertile ground for data mining by providing valuable source of information regarding the health status of a population. one of the most important public health functions is monitoring of a pulsation’s health Swills. At all levels of the health deli very 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 in to 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 Blaire Rural health Program- BRHP database at butajira, Ethiopia. A data mining tool called weak is used build predictive model of 95,220 cases over an eighteen-year r period. A historical cohort study analyst is of vital statistic is conducted. It follows a IDM process modeling. This study apply classification algorithm , such as to extract interesting knowledge from temporal data on BRI-I!> database. The results obtained in the study contain valuable new information. These results com-eyed some interesting findings. The class frication algorithm reveals that the res lust indicates for the BRHP-' dataset, over 90% accurate results are possible for developing class frication r les that call 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. III general. the result from this study is encouraging | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/7345 | |
| dc.language.iso | en | |
| dc.publisher | Addis Ababa University | |
| dc.subject | vital statistics data | |
| dc.subject | Machine Learning | |
| dc.subject | data mining | |
| dc.subject | predictive models | |
| dc.subject | classification | |
| dc.subject | Wake | |
| dc.title | Mining Vital Statistics Data: the Case of Butajira Rural Health Program | |
| dc.type | Thesis |