Author:
Hussain Arif,Malik Hassaan,Chaudhry Muhammad Umar
Abstract
Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.
Publisher
Taiwan Association of Engineering and Technology Innovation
Reference27 articles.
1. J. Mackay, G. A. Mensah, S. Mendis, and K. Greenlund, The Atlas of Heart Disease and Stroke, Geneva: World Health Organization, 2004.
2. Z. S. Wong, J. Zhou, and Q. Zhang, “Artificial Intelligence for Infectious Disease Big Data Analytics,” Infection, Disease, and Health, vol. 24, no. 1, pp. 44-48, February 2019.
3. S. Schneeweiss, “Learning from Big Health Care Data,” The New England Journal of Medicine, vol. 370, no. 23, pp. 2161-2163, June 2014.
4. A. Blumenthal, “Artificial Intelligence to Fight the Spread of Infectious Diseases,” https://phys.org/news/2018-02-artificial-intelligence-infectious-diseases.html, February 20, 2018.
5. K. E. Goodman, J. Lessler, S. E. Cosgrove, A. D. Harris, E. Lautenbach, J. H. Han, et al., “A Clinical Decision Tree to Predict Whether a Bacteremic Patient is Infected with an Extended-Spectrum β-Lactamase-Producing Organism,” Clinical Infectious Diseases, vol. 63, no. 7, pp. 896-903, October 2016.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献