Comparative Analysis and Evaluation of Machine Learning Algorithms for Early Detection of Cardiac Disease
Comparative Analysis and Evaluation of Machine Learning Algorithms for Early Detection of Cardiac Disease |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-8 |
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| Year of Publication : 2023 | ||
| Author : Ritika, Rajender Singh Chhillar, Sandeep Dalal |
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| DOI : 10.14445/22315381/IJETT-V71I8P222 | ||
How to Cite?
Ritika, Rajender Singh Chhillar, Sandeep Dalal, "Comparative Analysis and Evaluation of Machine Learning Algorithms for Early Detection of Cardiac Disease," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 253-262, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P222
Abstract
The diagnosis of Cardiac disease at the heart attack or stroke stage is very costly with lesser chances of survival. The online and automated healthcare system can be adapted to diagnose cardiac disease at an earlier stage. Various machine learning models are methods integrated within the health care systems to optimize the performance of cardiac disease prediction. In this paper, six supervised learning algorithms are implemented on an authenticated cardiac disease dataset. Bayesian network, decision tree, random forest, SVM, Neural network and Radial Basis Function classifiers are applied in this work on a filtered and preprocessed dataset. The analytical results are obtained in terms of accuracy, precision, recall, TP rate, FP rate and F-score parameters. The analysis results identified that the SVM and Naive Bayes classifiers achieved the most effective results. SVM with Gaussian kernel achieved the maximum accuracy rate, TP rate and least FP rate.
Keywords
Machine Learning, Supervised Learning, Cardiac Disease, Feature Selection, Classifiers.
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