Heart disease is the number one cause of death globally and people dying from heart disease have increased gradually through the years. heart disease is concertedly contributed by hypertension diabetes overweight and unhealthy lifestyles. Machine learning is widely used across many ranges around the world. The healthcare industry is not excluded. Machine learning can always play an essential role in predicting the presence or absence of locomotor disorders, heart diseases, and more. Search information if predicted well in advance can provide important intuitions to doctors who can adapt their diagnosis and deal per-patient basis. Since, heart-related diseases or cardiovascular diseases are the main reason for a huge number of deaths in the world over the last few decades and have emerged as the most life-threatening disease, not only in India but in the whole world so there is a need for a reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Portal machine learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data Machine learning techniques or algorithms used to predict heart diseases are logistic regression, SVC (support vector classifier), K neighbours classifier, nonlinear ml algorithms such as decision tree classifier, random forest classifier and gradient boosting classifier. Among the used models' the random forest algorithm is the best algorithm for the prediction of heart attack possibilities the researchers are accelerating their research works to develop software with the help of machine learning algorithms which can help doctors to decide both prediction and diagnosis of heart disease. The main objective of this research project is to predict the heart disease of patients using machine learning algorithms.
Software And Hardware