Enhancing Early Detection of Coronary Artery Disease with Machine Learning and Clinical Data Integration

The foremost step in the precise diagnosis of cardiovascular disease is the early and quick identification of patients showing heart disease. Most conventional cardiac diagnostic methods are very tedious and subjective, using lots of lab investigations and human interpretation. The present study, therefore, attempts to apply machine learning (ML) as one of the approaches for fast-tracking the diagnoses. We constitute several classification algorithms such as K-Nearest Neighbors (KNN), Random Forest, Naive Bayes Classifier, Support Vector Machine (SVM), and Decision Tree for examining basic clinical indicators of tobacco usage, obesity, blood sugar levels, and blood pressure. The aim of this development is to produce a highly accurate cardiovascular disease predictive model that may reduce and/or eliminate invasive tests, ensure rapid diagnosis, and improve patient outcomes.