Assessing the Impact of Various Machine Learning Algorithms for Heart Disease Prediction

Heart disease is amongst the key contributors to increasing mortality globally. Heart diseases affect many people of middle or old age, causing adverse effects such as stroke and heart attack in many cases. Therefore, effective diagnosis and diagnosis of heart disease is essential for prevention of serious health problems in the present scenario. In HDP (heart disease prediction), the futuristic probability of the coronary disease is forecasted based on the existing information. The heart disorder is predicted in diverse stages such as to pre-process the data, extract the attributes, and classify the data. This work concentrates on reviewing numerous techniques to predict the coronary disorders. The algorithms used for this work include Logistic Regression, Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest, XGBoost. In results Random Forest and XGBoost turn out to be the most effective algorithms, achieving an accuracy of 86.89% and 78.69%, respectively.