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Expert system based detection and classification of coronary artery disease using ranking methods and nonlinear attributes

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Abstract

This article presents an automated algorithm which combines ranking method with generalized discriminant analysis \((\mathrm{GDA})\) and1-norm extreme learning machine \((1-\mathrm{NELM})\) to detect coronary artery disease (CAD) subject. For detection of CAD, the eleven nonlinear attributes like correlation dimension \((\mathrm{CD})\), poincare plot, multivariate largest Lyapunov exponent \((\mathrm{MLLE})\), hurst exponent \((\mathrm{HE})\), Lempel–Ziv \((\mathrm{LZ})\), sample entropy (\(\mathrm{SampEn}\)), dispersion entropy \((\mathrm{DispEn}),\) improved permutation entropy (IPE), adaptive multiscale PE \((\mathrm{AMPE})\), multifractal detrended fluctuation analysis \((\mathrm{MFDFA}\)) and cumulative bi-correlation \((\mathrm{CBC})\) have been retrieved from heart rate variability (HRV) signal. For this analysis, the HRV data have been taken from publicly available database of healthy elderly (ELY), young \((\mathrm{YNG})\) and \(\mathrm{CAD}\) subjects. The rank of attributes have been calculated using ranking methods such as \(\mathrm{Fisher},\) \(\mathrm{Wilcoxon}\), Entropy, \(\mathrm{Bhattacharya}\), and receiver operating characteristic \((\mathrm{ROC})\). The experiments were carried out numerically through the combination of database sets YNG-ELY, \(\mathrm{YNG}-\mathrm{CAD}\) and \(\mathrm{ELY}-\mathrm{CAD}\) subjects. The numerical results have shown that ROC with GDA and \(1-\mathrm{NELM}\) approach achieved an accuracy of 99.76 ± 0.14, 99.87 ± 0.12 and 100 ± 0 respectivly for \(\mathrm{YNG}-\mathrm{CAD},\mathrm{ YNG}-\mathrm{ELY}\) and \(\mathrm{ELY}-\mathrm{CAD}\) groups. The Fisher with GDA and 1-NELM; and Bhattacharya with \(\mathrm{GDA}\) and \(1-\mathrm{NELM}\) approach achieved an accuracy of 100 ± 0 for all considered datasets. The proposed method also achieved very good generalization performance with the smallest 1-norm root mean square error (RMSE) and less execution validation time as compared to \(\mathrm{support vector machine}\) \((\mathrm{SVM})\) \(\mathrm{and probabilistic neural network}\) \((\mathrm{PNN})\).

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Authors and Affiliations

  1. Electronics & Communication Engineering Department (SIG), School of Electrical Engineering & Computing, ASTU, Adama, Ethiopia

    Ram Sewak Singh & Demissie Jobir Gelmecha

  2. Mechanical Design & Manufacturing Engineering Department, School of Mechanical, Chemical & Material Engineering, ASTU, Adama, Ethiopia

    D. K. Sinha

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  1. Ram Sewak Singh
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Singh, R.S., Gelmecha, D.J. & Sinha, D.K. Expert system based detection and classification of coronary artery disease using ranking methods and nonlinear attributes. Multimed Tools Appl 81, 19723–19750 (2022). https://doi.org/10.1007/s11042-021-11528-1

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