Prediction of cardiac attack using ensemble learning.

Autor:	Eranki, Kiran L. N., Kalerua, Sai Chandu, Pilli, Tejaswi, Alajpur, Chandana Priya, Tejaswini, Velpula
Předmět:	MACHINE learning DIETARY patterns SUPPORT vector machines K-nearest neighbor classification FEATURE selection RANDOM forest algorithms DATA privacy MYOCARDIAL infarction HUMAN fingerprints
Zdroj:	AIP Conference Proceedings; 2024, Vol. 2971 Issue 1, p1-9, 9p
Abstrakt:	The cardiac attack is considered to have high mortality due to lifestyle habits and dietary regimes. Prediction of heart disease based on medical history of patients is critical. As privacy of sensitive data is also of concern, digital fingerprint collected from vast amount of data generated by the healthcare sector is made easier with the help of Machine Learning (ML). With advances in technology application of internet of things (IoT) in healthcare is also gaining popularity. Application of algorithms to predict heart attack is also evident. In the current work, we propose to predict cardiac arrest using medical history for the patients data and select the features best suitable for based on a comparative analysis among the ML models. Different feature selection metrics and classification models are used for prediction. ML algorithms like Regression, Decision tree, Random Forest, Support vector machine, Naive Bayes, and K-nearest neighbor have been used. Our results show the importance of medical history in prediction of cardiac disease and diagnosis. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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