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Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

S. Kusuma*, Dr. Jothi K. R

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India

* Corresponding Author: S. Kusuma. Email: email

Computer Systems Science and Engineering 2022, 41(3), 1273-1289. https://doi.org/10.32604/csse.2022.021741

Abstract

One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like (i) Feature extraction, (ii) Feature selection, and (iii) Classification. At first, the feature extraction process is carried out, where the features like a time-domain index, frequency-domain index, geometrical domain features, nonlinear features, WT features, signal energy, skewness, entropy, kurtosis features are extracted from the input ECG signal. The curse of dimensionality becomes a severe issue. This paper provides the solution for this issue by introducing a new Modified Principal Component Analysis known as Multiple Kernel-based PCA for dimensionality reduction. Furthermore, the dimensionally reduced feature set is then subjected to a classification process, where the hybrid classifier combining both Recurrent Neural Network (RNN) and Restricted Boltzmann Machine (RBM) is used. At last, the performance analysis of the adopted scheme is compared over other existing schemes in terms of specific measures.

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APA Style
Kusuma, S., R, D.J.K. (2022). Heart disease classification using multiple K-PCA and hybrid deep learning approach. Computer Systems Science and Engineering, 41(3), 1273-1289. https://doi.org/10.32604/csse.2022.021741
Vancouver Style
Kusuma S, R DJK. Heart disease classification using multiple K-PCA and hybrid deep learning approach. Comput Syst Sci Eng. 2022;41(3):1273-1289 https://doi.org/10.32604/csse.2022.021741
IEEE Style
S. Kusuma and D.J.K. R, "Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach," Comput. Syst. Sci. Eng., vol. 41, no. 3, pp. 1273-1289. 2022. https://doi.org/10.32604/csse.2022.021741



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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