Abstract
Disparate types of data including biological and environmental have been used in supervised learning to predict a specific disease outcome. However, social determinants of health, which have been explored very little, promise to be signi?cant predictors of public health problems such as malaria and anemia among children. We considered studying their contribution power in malaria and anemia predictions based on Variable Importance in Projection (VIP). This innovative method has potential advantages as it analyzes the impact of independent variables on disease prediction. In addition, we applied ?ve machine learning algorithms to classify both diseases, using social determinants of health data, and compared their results. Of them all, arti?cial neural networks gave the best results of 94.74% and 84.17% accuracy for malaria and anemia prediction, respectively. These results are consistent and reflect the signi?cance of non-medical factors in disease prediction.