DECISION SUPPORT SYSTEM FOR THE DIAGNOSIS OF CHOLERA USING SUPERVISED LEARNING

Abstract

A machine learning application was developed in this study for the pre-diagnosis of cholera. The independent variables used for the training dataset include symptoms such as diarrhea, vomiting dehydration as well as nauseas. The variables were assigned numeric values and twenty five percent of the total dataset were used for training and testing the classifier algorithm. After training the algorithm was able to predict the presence of cholera or otherwise depending of the new dataset presented to the classifier algorithm

Identifiers

Resource ID

urn:uuid:a278d905-350e-4bb6-a477-76ef0fbd450d