PREDICTION OF MALARIA SPECIES MORBIDITY USING DATA MINING TECHNIQUE: THE CASE OF CHEWAKA HEALTH CENTER ILU ABA BORA ZONE, OROMIA NATIONAL REGIONAL STATE, SOUTH WEST ETHIOPIA, 2012.

Abstract

Abstract Introduction: - Malaria is continues to be a leading cause of morbidity and mortality worldwide. In Ethiopia, transmission is unstable and seasonal, with occasional devastating epidemics. In health facilities morbidity data can be used for prediction of occurrence of disease and can help in decision making using data mining techniques. Objective: - The main objective of this study was to predict malaria species morbidity from malaria data by using data mining technique in Chewaka Health Center, South-West Ethiopia, 2012. Methods: Institution based retrospective record review study was conducted. All malaria positive data in Chewaka health center from September 01, 2007 to December 30, 2011 was collected from manual records in to new format prepared for the study purpose and the data was integrated with meteorology data of nearby meteorology station. Data quality was assured by using cross-check up of data collected with manual available record. A total of 5077 records were used and data analysis was done by using WEKA classification decision tree J48 and neural network algorisms with two modes, 10 fold cross-validation and 90%-10% percentage split for training-testing modes. Results: Of 5077 records in dataset class attribute accounts P. faliciparum 2745(54.1%), P. vivax 2258(44.5%) and mixed 74 (1.5%). Prediction model developed by Decision tree J48 algorism with 90%-10% training-test mode was scored the highest accuracy and selected as best model. The model predicted correctly 86.22 % P. faliciparum, P. vivax 86.14 %and as mixed 99.4% species in their class, over all predictive accuracy was 90.5%. Mean monthly relative humidity, mean monthly maximum temperature, total monthly rainfall, Age and address were selected by the models as best predictive attributes for malaria species. Conclusion and recommendation:-This study showed malaria morbidity to be predicted by mean monthly relative humidity, mean monthly maximum temperature, total monthly rainfall, Age and address. Using meteorology and morbidity data mining that can help for effective decision making on malaria prevention and early warning of epidemics is recommended.