Fusion of Classification with Hybrid Optimization Technique to Predict Diabetes

Abstract

The main objective of this paper is to predict diabetes which is growing like an epidemic in India. The key focus is to envisage diabetic and normal patient using classification approach .Fusion of SVM enhanced with hybrid optimization of PSO-BAT algorithm is proposed. Classification techniques used namely Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), Random Forest (RF) are compared with our novel approach SVM enhanced with hybrid optimization of PSO-BAT algorithm. The accuracy is increased using the combination technique. The benchmark diabetic dataset, PIMA Indian Diabetes Dataset from UCI machine learning repository is utilized for the research. To improve the efficiency more, classifiers such as Precision, Recall and f-measure is used.