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

作     者：S.Kusuma Dr.Jothi K.R 

作者机构：School of Computer Science and EngineeringVellore Institute of TechnologyVellore632014India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第41卷第6期

页      面：1273-1289页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Heart disease prediction ecg recurrent neural network pca restricted boltzmann machine 

摘      要：One of the severe health problems and the most common types of heartdisease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurswithout any symptoms, it cannot be cured by an intelligent detection *** effective diagnosis and detection of CHD should prevent human ***, intelligent systems employ clinical-based decision support approachesto assist physicians in providing another option for diagnosing and detecting *** paper aims to introduce a heart disease prediction model including phaseslike (i) Feature extraction, (ii) Feature selection, and (iii) Classification. At first,the feature extraction process is carried out, where the features like a time-domainindex, frequency-domain index, geometrical domain features, nonlinear features,WT features, signal energy, skewness, entropy, kurtosis features are extractedfrom the input ECG signal. The curse of dimensionality becomes a severe *** paper provides the solution for this issue by introducing a new ModifiedPrincipal Component Analysis known as Multiple Kernel-based PCA for dimensionality reduction. Furthermore, the dimensionally reduced feature set is thensubjected to a classification process, where the hybrid classifier combining bothRecurrent Neural Network (RNN) and Restricted Boltzmann Machine (RBM)is used. At last, the performance analysis of the adopted scheme is compared overother existing schemes in terms of specific measures.