Published Paper Details:

Heart diseases are considered one of the most familiar causes of death worldwide. Early identification and medication can save a lot of people. There are multiple types of heart diseases that make it difficult to identify the type of disease that a patient is suffering with. Such data, whenever anticipated well ahead of time, can give significant instincts to specialists who can then adjust their conclusion and manage per patient premise. A machine must be developed such that it can identify the type of heart disease and update the machine itself by taking the experiences of the patients. (ML) can bring a compelling answer for navigation and precise forecasts. The clinical business is showing gigantic advancement in utilizing AI strategies. We work on foreseeing conceivable heart diseases in individuals utilizing Machine Learning calculations. This paper aims to build a model using multiple machine learning classifiers such as Logistic Regression (LR), Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) & Decision Tree. The model is able to implement hybrid classification by gaining weak and strong classifiers trained and tested on a dataset that contains multiple attributes and the efficient algorithm is considered the best. The decision tree performed best among the other algorithms with better accuracy and performance.