Automated heart disease prediction model by hybrid heuristic-based feature optimization and enhanced clustering. (February 2022)

• Record Type: Journal Article
• Authors: Sonawane, Ritesh; Patil, Hitendra
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: Heart disease prediction -- Discrete wavelet transform -- Numerical data and electrocardiogram -- Jaya algorithm with red deer algorithm -- K-means clustering -- Optimal feature extraction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103260
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Exploits a new algorithm called Jaya Algorithm with Red Deer Algorithm (J-RDA). Hybrid clustering is formed by integrating the optimized DBSCAN and optimized KMC. Promotes a novel heart diseases prediction using ECG signals and data attributes. Abstract: The main intent of this paper is to implement a novel clustering model for heart disease prediction with numerical data and ECG signals using an optimal feature extraction approach. Rather than the direct use of numerical data to clustering, the Electrocardiogram (ECG) signals are initially subjected for the signal decomposition using Discrete Wavelet Transform (DWT), and dimensionality reduction is performed through Principal Component Analysis (PCA). Both the data are processed for the optimized feature extraction stage. Here, the hybrid meta-heuristic concept is adopted for the optimized feature extraction based on Jaya Algorithm with Red Deer Algorithm (J-RDA). Once the feature optimization is done, the hybrid clustering is formed by integrating the optimized Density-based Spatial Clustering of Applications with Noise (DBSCAN) and optimized K-Means Clustering (KMC), in which the proposed J-RDA is used for tuning the significant parameters. Moreover, the objective model for feature optimization and optimized hybrid clustering for proposed heart disease prediction tries to solve the multi-objective function. The results reveal that the proposed model achieves good performance in rectifying the problems in heart disease prediction for dual data types.