DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-5 ( May, 2022 ) | |||||||||
Paper Title |
Heart Disease Prediction using Machine Learning | |||||||||
Author Name |
M.Sathvik Reddy, Y.Sai Nithin, Priscilla Joy, Roshini Thaka | |||||||||
Affilition |
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Pages |
32-38 | |||||||||
Abstract |
Abstract - Heart disease is a complicated condition that affects a large number of individuals throughout the world. In healthcare, particularly in the field of cardiology, timely and accurate diagnosis of cardiac disease is critical. In this study, we suggested a method for diagnosing cardiac illness that is both efficient and accurate, and it is based on machine learning techniques. The system is developed based on classification random forest and Decision tree while standard features selection algorithms have been used such as, least absolute shrinkage ,Minimal redundancy, Relief, maximal relevance, selection operator and Local learning for removing irrelevant and redundant features. The features selection techniques are used to boost the classification accuracy and lower the classification system's execution time. In addition, the leave one subject out cross-validation approach has been utilised to discover best practises in model assessment and hyperparameter tuning. Keywords – Minimal redundancy, Maximal relevance, Shrinkage, cardiac disease | |||||||||
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