Machine learning classification of infectious disease distribution status

Infectious diseases are common and caused by microorganisms such as viruses, bacteria, parasites. Indicators of the spread this disease can be seen based on population level number confirmed cases. This study aims to develop a machine learning (ML) analysis model using K-means cluster, artificial neural network (ANN), decision tree (DT) methods. The dataset used in was obtained patients distribution population. process is divided into two stages, namely preprocessing classification process. pre-processing stage produce pattern that describe status. will continued at ANN DT. Classification gave significant results with an accuracy rate 99.77%. also knowledge tree. Overall, contribution research presents latest information knowledge. presented have impact control environmental management public health.