Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease

Abstract

Vector borne diseases like malaria fever is one of the most elevating issues in medical domain. Accurate identification of a patient from the given set of samples and classification becomes one of the challenging task when dealing with imbalanced datasets. Many conventional machine learning and data mining algorithms are shows poor performance to classify skewed distributed data because they are trained very well with the majority class samples only. Proposing an ensemble method called majority voting defined with a set of machine learning algorithms namely decision tree—C4.5, Naive Bayesian and K-Nearest Neighbor (KNN) classifiers. Classification of samples can be done based on the majority voting of classifiers. Experiment results stating that voting ensemble method shows classification accuracy of 95.2% on imbalanced malaria disease data whereas dealing with balanced malaria disease data voting ensembler shows 92.1% of accuracy. Consequently voting shows 100% classification report on precision, Recall and F1-Score on imbalanced malaria disease data sets whereas on balanced malaria disease data voting shows 96% of Precision, Recall and F1-Score metrics.