A novel enhanced prediction of possibility for cardiac arrest in cardiovascular disease of heart patients by comparing support vector machine over decision tree.

Autor:	Reddy, Vonteddu Vijendra, Kumar, S. Udhaya
Předmět:	SUPPORT vector machines DECISION trees CARDIAC patients CARDIAC arrest CARDIOVASCULAR diseases HEART
Zdroj:	AIP Conference Proceedings; 2024, Vol. 2853 Issue 1, p1-9, 9p
Abstrakt:	The goal is to anticipate cardiac arrest in heart disease patients. Machine learning methods like SVM and decision tree categorise photos (DT). To effectively and reliably analyse labelled pictures with G power of 80%, threshold 0.05 percent, CI 95 percent mean and standard deviation, SVM and Decision Tree sample sizes of n=5 were iterated 10 times. The Support Vector Machine (SVM) outperformed the Decision Tree (DT) in predicting and categorising cardiac patients' data with a p-value of 0.05. Support Vector Machine predicts cardiomyopathy risk better than Decision Tree. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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