Heart Disease Prediction and Analysis using various Machine Learning Algorithms

Fact that heart disease still significantly influences world mortality rates emphasizes the vital necessity of effective prediction and analytic instruments. This work explores use of multiple ML algorithms to issue of heart disease risk prediction using patient data in order to enhance accuracy and dependability. Our enormous dataset comprised demographic data in addition to health-related characteristics. Among the tests and evaluations for their predictive power was logistic regression, SVM, decision trees, random forest, along with KNN. Based on studies, certain algorithms an especially ensemble technique offers superior resilience and accuracy and feature selection in improving model performance. Results highlight the possibility of machine learning for improved, early diagnosis of heart diseases, which would direct more focused therapy. This paper contributes to the growing field of healthcare analytics by clarifying the optimal machine learning methods for cardiac issue prediction. With a 96.5% accuracy rate and lessening of detection time, the model exceeded traditional techniques in accuracy, precision, recall, along with F1 score.