ENHANCED PREDICTIVE MODEL FOR SCHISTOSOMIASIS

Abstract

Neglected Tropical Diseases (NTDs) are wide spread diseases found in many countries in Africa, Asia and Latin America, they are mostly found in tropical areas where people have no access to clean water or safer ways to dispose of human waste. Schistosomiasis is one of the NTDs. Data mining is used in extracting rules to predict certain information in many areas of Information Technology, medical science, biology, education, and human resources. Classification is one of the techniques of Data mining. In this work, we used three classifiers namely; Naïve Bayes, Support Vector Machine and Logistic Regression to design a framework for classifying and predicting the status of Schistosomiasis and its complications in a suspected patient using their clinical data. For the purpose of this study, we considered the parameters: Abdominal, Diarrhea, Bloody_stool, Bloody urine, Swim, Dam_river_ use, Urinating_stool_in_water, Boil_water_use. The framework was trained using data acquired from Federal Medical Centre Katsina and NTD unit of Katsina State ministry of health, to test for performance accuracy. The research shown that out of the three classifiers, Logistic Regression performed better by having 97.8% accuracy.