Prediction of Childhood Diarrhea in Bangladesh using Machine Learning Approach

Diarrhea has remained a major health problem among under-five (U5) children that leads high level of morbidity and mortality. This study is to determine the socio-demographic risk factors of diarrhea as well as predict of diarrhea status using machine learning (ML) based approach among U5 children in Bangladesh. Bangladesh Demographic and Health Survey, 2014 dataset is used in this study. This dataset consisted of 7,538 respondents who had 371 (4.9%) child’s diarrhea. Logistic regression (LR) is used to determine the high-risk factors of diarrhea. Then four ML-based approach namely naïve Bayes (NB), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) was applied to predict the child’s diarrhea status and accuracy, sensitivity, and specificity are used to evaluate the performance of these classifiers. Around 4.9% women reported that their children have experienced an episode of diarrhea in two weeks before the survey. LR model showed that the child’s age, region (Khulna and Rangpur), mothers who had completed secondary education, and respondents who were rich wealth index, significantly associated risk factors for diarrhea disease. Our findings indicate that SVM with radial basis kernel yielded 65.61% accuracy, 66.27% sensitivity, and 52.28% specificity which are comparatively better than others. The prevalence of diarrhea disease is more common among Bangladeshi children. Our study shows that SVM is capable of predicting child diarrhea status (generally highly imbalanced data). This study allows policy makers towards appropriate decisions to reduce childhood diarrhea in Bangladesh.