Published Paper Details:

Machine Learning is finding applications in a wide number of disciplines throughout the world, including the healthcare industry. It can be used to predict the existence or lack of locomotor disorders, cardiac illnesses, and other conditions. When these assumptions are established well in advance, it means they can provide vital insights to clinicians, allowing them to personalise diagnoses and therapies to each individual patient. The goal of this study is to apply algorithms based on machine learning to anticipate possible cardiac problems in individuals. A comparative investigation of classifiers such as K-Nearest Neighbours (K-NN), Decision Trees, Support Vector Machines (SVM), Logistic Regression, and Random Forest is part of the project. It also presents an ensemble classifier which employs both strong as well as weak classifiers to conduct hybrid classification. This method is helpful since it allows for the incorporation of a wide range of validation and training samples, which leads to improved precision and predictive analysis. This Model accepts as input the qualities that are kept after the feature selection techniques have been applied. Only 14 of the 75 features that make up the UCI Heart Disease data set are chosen and utilised for prediction. This model predicts whether a person has heart disease or not.