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Machine Learning methods for Heart Disease

Machine learning plays an indispensable role in predicting the occupancy of loco-motor disorders, Heart disease, and effective tool in prophecy the survival of patients with heart failure symptoms. The necessity of a heart disease prediction system is to establish the relationship between the medical factors and patterns related to heart disease. The predicted information provides important insights to doctors and adapts their diagnosis for each individual. Heart Disease or Cardiovascular disease is the primary basis of death worldwide and emerging as a life-threatening disease. Various habitual risk factors such as physical inactivity, smoking, stress, overuse of alcohol and caffeine, along with other physiological factors like high blood cholesterol, obesity, hypertension, and pre-existing heart conditions, are predisposing aspects for heart disease. The accurate and efficient early medical diagnosis of heart disease plays an important role in taking preventive measures to avoid death. The goal of machine learning methods is to predict heart disease by processing patient datasets, and data of patients need to predict the chance of occurrence of heart disease. Machine learning methods and data mining are the most commonly used techniques for predicting heart disease. Machine learning extracts valuable information from a dataset by various learning techniques such as regression, clustering, and association rule. Support Vector Machine, Gradient Boosting, Random Forest, Naive Bayes classifier, logistic regression, decision tree, K-nearest neighbor, and ensemble model are the machine learning algorithms used to classify various heart disease attributes in predicting heart disease. Naive Bayes and decision trees are the best algorithms regarding accuracy level in heart disease. Recent advancements in heart disease predictions using machine learning are performance evaluation of heart disease predictions, accurate machine learning models for congenital heart disease, and integration of deep learning networks with machine learning.