Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania

DM practices in medical sciences have brought about improved performance in analysis of large and complex datasets. DM facilitates evidence-based medical hypotheses. Nowadays, health diseases, especially obstetric fistula, are increasing. CCBRT reports, approximately 3,000 women suffer from obstetric fistula annually. Since efforts to eradicate obstetric fistula have been inadequate, the researcher was motivated to employ MLA in BIO informatics to detect obstetric fistula. The purpose of this chapter was to use DM techniques to predict obstetric fistula. The datasets involving 367 patient records from January 2015 to February 2019 were collected from CCBRT. The environment was used to describe the accurate of predictive model was CV, ROC, and CM. The research was performed using six different MLA. The accuracy performance between algorithms shows that LR has better accuracies of 87.678%, precision measures of 91%, recall measures of 82%, f1-score measures of 86%, and support measures of 74%. Thus, the researcher chose to use LR as the proposed obstetric fistula prediction model.