INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Coronary artery heart Disease (CAD) is caused by atherosclerosis in coronary arteries and results in cardiac arrest and heart attack. For diagnosis of CAD, angiography is used which is a costly time consuming and highly technical invasive method. Researchers are therefore, prompted for alternative methods such as machine learning algorithms that could use non-invasive clinical data for the heart Disease diagnosis and assessing its severity. In this study, we present a novel hybrid method for CAD diagnosis, including risk factor identification using correlation based feature subset (CFS) selection with particle swam optimization (PSO) search method and K-Means clustering algorithms. Supervised learning algorithms such as multi-layer perceptron (MLP), multinomial logistic regression (MLR), fuzzy unordered rule induction algorithm (FURIA) and C4.5 are then used to model CAD cases. We tested this approach on clinical data consisting of 26 features and 335 instances collected at the Department of Cardiology, Indira Gandhi Medical College, and Shimla, India. MLR achieves highest prediction accuracy of 88.4 %.We tested this approach on benchmarked Cleveland heart Disease data as well. In this case also, MLR, outperforms other techniques. Proposed hybridized model improves the accuracy of classification algorithms from 8.3 % to 11.4 % for the Cleveland data. The proposed method is, therefore, a promising tool for identification of CAD improved prediction accuracy.
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Cite Article:
"Heart Disease Identification Method using Machine Learning Classification in E-Healthcare ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.6, Issue 4, page no.448 - 450, April-2021, Available :http://www.ijsdr.org/papers/IJSDR2104073.pdf
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Publication Details:
Published Paper ID: IJSDR2104073
Registration ID:193194
Published In: Volume 6 Issue 4, April-2021
DOI (Digital Object Identifier):
Page No: 448 - 450
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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