Malaria Outbreak Detection with Machine Learning Methods

Abstract

AO_SCPLOWBSTRACTC_SCPLOWIn this paper, we utilized and compared selected machine learning techniques to detect malaria out-break using observed variables of maximum temperature, minimum temperature, humidity, rainfall amount, positive case, and Plasmodium Falciparum rate. Random decision tree, logistic regression, and Gaussian processes are specially analyzed and adopted to be applied for malaria outbreak detection. The problem is a binary classification with outcomes of outbreak or no outbreak. Sample data provided in the literature from Maharashtra, India is used. Performance of the models are compared with the results from similar studies. Based on the sample data used, we were able to detect the malaria outbreak without any false positive or false negative errors in the testing dataset.