Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases
This work addresses early diagnosis of coronary heart disease, a leading cause of death, but is incremental as it applies existing methods to a medical dataset.
The study tackled the prediction of coronary heart diseases by comparing machine learning classification algorithms, finding that logistic regression achieved the highest performance score on the original dataset.
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.