Evaluating the Effective Machine Learning Techniques for Early Prediction of Heart Disease

Machine learning has found applications in numerous fields across the globe its use in healthcare has proven to be highly valuable, particularly in diagnosing conditions like heart disease and locomotor disorders. This research study focuses on leveraging the popular algorithms in machine learning for the prediction of potential heart diseases in patients. The study includes a comparative analysis of various classifiers, namely decision trees, Naive Bayes, logistic regression, Support Vector Machines (SVM), and Random Forest (RF). The goal is to categorize the maximum effective classifier for accurate and consistent predictions. Additionally, the research proposes an ensemble classifier that combines both strong and weak classifiers. This hybrid approach aims to take advantage of a large number of training and validation samples, enhancing the classifier's performance. The results of the comparative analysis and the proposed ensemble classifier hold promise for advancing the field of medical diagnosis and treatment customization. Ultimately, the incorporation of techniques in the healthcare sector, machine learning has the potential to significantly advance patient outcomes and overall healthcare quality.