Prediction of Cardiovascular Disease using Machine Learning Algorithms

Abstract

Heart is the most critical organ of our body for being responsible for regulating and maintaining the blood circulation levels. Globally, heart disease cases are prevalent and constitute a significant cause of mortality. Manifestations such as chest discomfort and irregular heartbeat are notable symptoms. The healthcare sector has amassed substantial knowledge in this domain. Analyzing the research, this paper delves into the concept of utilizing ML algorithms to predict cardiac diseases. In this research will employ a diverse array of machine learning techniques, including decision tree, support vector classifier, random forest, K-NN, logistic regression and naive Bayes. These algorithms utilize specific characteristics to forecast cardiac diseases effectively. Leveraging machine learning algorithms to analyze and predict outcomes from the extensive healthcare-generated data shows considerable promise. Recent advancements in machine learning models have incorporated numerous features, and in this study, propose the integration of these features in machine learning algorithms to forecast cardiovascular ailments. The main objective of this research is to identify the performance of the mentioned machine learning algorithms for predicting cardiovascular elements.