ABSTRACT

Today machine learning is playing an important role especially in the healthcare field. Heart disorders, generally referred to as cardiovascular diseases are the main cause of death in the world. The number of tests required for the detection of heart disease is decreasing due to machine learning techniques. This paper looks at heart failure survivors from a group of 299 individuals who were hospitalised to the hospital. The goal is to use of machine learning models that can enhance the predictability of cardiac patient survival. In this paper, we have evaluated the accuracy of seven machine learning methods for cardiac illness prediction, including Nave Bayes (NB), Decision Tree (DT), K Nearest Neighbour (KNN) and Logistic Regression (LR), Random Forest (RF), Extra Tree (ET) and Ridge Classifiers (RC). The comparative study has proven Random Forest (RF) with a maximum accuracy (87.77%) with the lowest error rate. Keywords: Cardiovascular diseases, Nave Bayes, Decision Tree, K Nearest Neighbour, Logistic Regression, Random Forest, Extra Tree, Ridge Classifiers
Publication date: 01/06/2023

https://ijbpas.com/pdf/2023/June/MS_IJBPAS_2023_7183.pdf

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https://doi.org/10.31032/IJBPAS/2023/12.6.7183