A novel approach for heart disease prediction using hybridized AITH2O algorithm and SANFIS classifier.

Abstract

In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendations from medical professionals. However, the existing machine learning classifiers suffer from computational complexity and overfitting problems, which reduces the classification accuracy of the diagnostic system. To address these constraints, this work proposes a new hybrid optimization algorithm to improve the classification accuracy and optimize computation time in smart healthcare applications. Primarily, the optimal features are selected through the hybrid Arithmetic Optimization and Inter-Twinned Mutation-Based Harris Hawk Optimization (AITH²O) algorithm. The proposed hybrid AITH²O algorithm entails advantages of both exploration and exploitation abilities and acquires faster convergence. It is further employed to tune the parameters of the Stabilized Adaptive Neuro-Fuzzy Inference System (SANFIS) classifier for predicting heart disease accurately. The Cleveland heart disease dataset is utilized to validate the efficacy of the proposed algorithm. The simulation is carried out using MATLAB 2020a environment. The simulation results show that the proposed hybrid SANFIS classifier attains a superior accuracy of 99.28% and true positive rate of 99.46% compared to existing state-of-the-art techniques.