Application Of Data Mining For Predicting Adult Mortality.

Background: The fast-growing, tremendous amount of data, collected and stored in large andrnmassive data repositories, has far exceeded human ability for comprehension without powerfulrntools. As a result, data collected in large data repositories become seldom visited. This in turn,rncalls the application of data mining technology. Every year, more than 7·7 million children diernbefore their fifth birthday. However, over three times those of nearly 24 million adults die everyrnyear. Less attention has been given to adults which are the most productive phase of life for bothrneconomic and social ramification of families and countries. rnObjective: The general objective of this research is to construct adult mortality predictive modelrnusing data mining techniques so as to identify and improve adult health status using BRHP openrncohort database.rnMethods: The hybrid model that was developed for academic research was followed. Dataset isrnpreprocessed for missing values, outliers and data transformation. Decision tree and Naïve Bayesrnalgorithms were employed to build the predictive model by using a sample dataset of 62,869rnrecords of both alive and died adults through three experiments and six scenarios. rnResult: In this study as compared to Bayes, the performance of J48 pruned decision tree revealsrnthat 97.2% of accurate results are possible for developing classification rules that can be used forrnprediction. If no education in family and the person is living in rural highland and lowland, thernprobability of experiencing adult death is 98.4% and 97.4% respectively with concomitantrnattributes in the rule generated. The likely chance of adult to survive in completed primaryrnschool, completed secondary school, and further education is (98.9%, 99%, 100%) respectively. rnConclusion: The study suggests that education plays a considerable role as a root cause of adultrndeath, followed by outmigration. Further comprehensive and extensive experimentation isrnneeded to substantially describe the loss experiences of adult mortality in Ethiopia.

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