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Title |
Comparative Analysis and Evaluation of Machine Learning Algorithms for Early Detection of Cardiac Disease |
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Author |
Ritika, Rajender Singh, Sandeep Dalal |
| Citation |
Vol. 26 No. 4 pp. 103-110 |
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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. |
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Keywords |
Machine Learning; Supervised Learning; Cardiac Disease; Feature Selection; Classifiers |
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