Prediction of cardiac disease using online extreme learning machine Online publication date: Mon, 20-Dec-2021
by Sulekha Saxena; Vijay Kumar Gupta; P.N. Hrisheekesha; R.S. Singh
International Journal of Computer Applications in Technology (IJCAT), Vol. 66, No. 2, 2021
Abstract: This paper presents an automated Machine Learning (ML) algorithm to detect the Coronary Disease like Congestive Heart Failure (CHF) and Coronary Artery Disease (CAD). The proposed method has been employed as a combination of non-linear feature extraction methods: Online Sequential machine (OS-ELM) and Linear Discriminate Analysis (LDA) as well as Generalised Discriminate Analysis (GDA) as feature reduction algorithms. For training and validation of ML, 12 non-linear features were extracted from Heart Rate Variablility (HRV) signal. The numerical experiments were carried out on the sets as CAD-CHF, YOUNG-ELDERLY-CAD and YOUNG-ELDERLY-CHF subjects. The numerical simulation results clearly have shown that GDA combined with OS-ELM gives better detection performance compared to OSELM. To test the robustness of proposed method the classification performance like accuracy, positive prediction value, sensitivity and specificity were calculated on 100 trials and it achieved average performance accuracy of 99.77% for YOUNG-ELDERLY-CAD and 100% for CAD-CHF and YOUNG-ELDERLY CHF subjects.
Online publication date: Mon, 20-Dec-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com