  Heart disease is a serious global health issue that claims millions of lives
every year. Early detection and precise prediction are critical to the
prevention and successful treatment of heart related issues. A lot of research
utilizes machine learning (ML) models to forecast cardiac disease and obtain
early detection. In order to do predictive analysis on "Heart disease health
indicators " dataset. We employed five machine learning methods in this paper:
Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra
Tree Classifier, and AdaBoost. The model is further examined using various
feature selection (FS) techniques. To enhance the baseline model, we have
separately applied four FS techniques: Sequential Forward FS, Sequential
Backward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling
is applied to the models to enable additional analysis. The findings show that
when it came to predicting heart disease, ensemble approaches in particular,
random forests performed better than individual classifiers. The presence of
smoking, blood pressure, cholesterol, and physical inactivity were among the
major predictors that were found. The accuracy of the Random Forest and
Decision Tree model was 99.83%. This paper demonstrates how machine learning
models can improve the accuracy of heart disease prediction, especially when
using ensemble methodologies. The models provide a more accurate risk
assessment than traditional methods since they incorporate a large number of
factors and complex algorithms.
