Multi-objective differential evolution based random forest for e-health applications

Abstract

Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.