Cardiac Risk Prediction Using Extra Trees-Based Classifier

Heart Disease, also known as CVD, represents one of the major health issues worldwide. There is an urgent need for the availability of risk prediction systems that are reliable yet non-invasive in nature to facilitate timely clinical interventions. This study aims to explore how well the Extra Trees Classifier performs in predicting the risk of heart disease. The Extra tree-based method represents an advanced ensemble ML approaches that was trained on a comprehensive dataset containing 20 key clinical and lifestyle attributes of patients. In addition, this approach was carefully tuned and thoroughly evaluated to ensure reliable performance. Feature analysis plays the main role in this paper by ranking the most influential predictors of CVD risk according to their importance. This allows drawing data-driven conclusions that can inform clinically oriented risk assessment analyses. Based on the results described above, the Extra Trees Classifier is effective and reliable for predictive cardiology and thus serves as a good starting point for improved clinical decision-making.