An Analysis of Performance and Dataset Dynamics in the Early Detection of Cardiovascular Diseases

Cardiovascular Diseases (CVD) are the most prevalent global health concern that demands p romp t at t ent ion given their substantial role in increasing mortality figures. Owing to the need for early detection to alleviate the inimical effects of CVD, this study makes extensive use of machine learning techniques including Support Vector Machine (SVM), AdaBoost, XGBoost, and Decision Tree in the early prediction of cardiovascular diseases. The robustness of the model will be enhanced by assessing three diverse datasets enriched with various types of patient information to derive the most efficient model. Through this study we conduct thorough performance evaluations, considering various evaluation metrics such as Accuracy, Sensitivity and False positive rate, aiming to identify the most effective machine learning model for early CVD detection. The results help shed light on important findings that can lead to improved outcomes, which help in the fight against cardiovascular diseases.