Application of Data Mining Techniques to Predict Adult Mortality: The Case of Butajira Rural Health Program, Butajira, EthiopiaTesfahun Hailemariam1*, Million Meshesha2 and Alemayehu Worku3
- *Corresponding Author:
- Tesfahun Hailemariam
Department of Health Informatics
Hawassa Health Science College
E-mail: tesfahunhailemariam @gmail.com
Received date: May 27, 2015 Accepted date: July 24, 2015 Published date: July 31, 2015
Citation: Hailemariam T, Meshesha M, Worku A (2015) Application of Data Mining Techniques to Predict Adult Mortality: The Case of Butajira Rural Health Program,Butajira, Ethiopia. J Health Med Informat 6:197. doi: 10.4172/2157-7420.1000197
Copyright: © 2015 Hailemariam T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Though adults are care providers and risk takers of a society, reports indicate that adult mortality conditions are not given much emphasis. This is due to a widespread perception that mortality among adults is low. Every year, more than 7•7 million children die before their fifth birthday; however, nearly 24 million of adults die under the age of 70 years. Identifying major determinants for adult death helps to alleviate the loss of the productive group. Therefore, this research is aimed to apply data mining techniques to build a model that can assist in predicting adult health status.
Methods: The hybrid model that was developed for academic research was followed. Dataset was preprocessed for data transformation, missing values and outliers. WEKA 3.6.8 data mining tools and techniques such as J48 decision tree and Naïve Bayes algorithms were employed to build the predictive model by using a sample dataset of 62,869 instances of both alive and died adults through three experiments and six scenarios. The area under the ROC curve for outcome class is used to evaluate performances of models from the predictive algorithms.
Results: In this study as compared to Bayes, the performance of J48 pruned decision tree reveals that 97.2% of accurate results are possible for developing classification rules that can be used for prediction. If no education in family and the person is living in rural highland and lowland, the probability of experiencing adult death is 98.4% and 97.4% respectively with concomitant attributes in the rule generated. The likely chance of adult to survive in completed primary school, completed secondary school, and further education is (98.9%, 99%, 100%) respectively.
Conclusion: Predictive model built with the use of data mining techniques suggests that education plays a considerable role as a root cause of adult death, followed by outmigration. The possibility of incorporating the findings of this study with knowledge based system should be explored so that experts can consult the system in their problem solving and decision making process. Further comprehensive and extensive experimentation is needed to substantially describe the loss experience of adult mortality in Ethiopia.