Prediction of Risk in Cardiovascular Disease using Machine Learning Algorithms

Prediction of heart disease is one of the most complex tasks in medical field and prior detection of heart disease become an area of research to save patient lives. During the pandemic period, the number of cardiac arrest cases at home has drastically increased due to inaccurate predictions and delay in seeking medical attention. The health care industry works on processing of huge data and the solution for this is machine learning. Data science process large amount of data to make intelligent health care decisions thereby avoids risk and alert the patients. In this paper, a comparative analysis of different machine learning classifiers based on dataset to predict the chance of heart disease with minimal attributes. The ML algorithms used for HD prediction are K- Nearest Neighbour, Gradient Boosting Classifier, Support Vector Machine, Naive Bayes, Logistic Regression and Random Forest algorithm. The paper also finds the correlation between different attributes and hence using them efficiently for prediction of heart attack.