  Coronary heart disease, which is a form of cardiovascular disease (CVD), is
the leading cause of death worldwide. The odds of survival are good if it is
found or diagnosed early. The current report discusses a comparative approach
to the classification of coronary heart disease datasets using machine learning
(ML) algorithms. The current study created and tested several
machine-learning-based classification models. The dataset was subjected to
Smote to handle unbalanced classes and feature selection technique in order to
assess the impact on two distinct performance metrics. The results show that
logistic regression produced the highest performance score on the original
dataset compared to the other algorithms employed. In conclusion, this study
suggests that LR on a well-processed and standardized dataset can predict
coronary heart disease with greater accuracy than the other algorithms.
