Comparing some Machine Learning Models for Cardiovascular Disease

Background and Aim: Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, necessitating the development of accurate predictive models for early diagnosis. Therefore, this study aimed to evaluate and compare the performance of three machine learning models-Random Forest, Decision Tree, and K-Nearest Neighbors-in predicting cardiovascular disease based on key risk factors. Method: This retrospective study utilized patient data from Shar Hospital in Sulaimaniyah City. The dataset included demographic and clinical risk factors such as age, smoking status, diabetes, hypertension, and family history of cardiovascular disease. The three machine learning models were trained and tested using various data-splitting ratios, and their performance was assessed using accuracy, F1-score, recall, precision, and specificity. Statistical analysis and model validation were conducted using Python in Jupyter Notebook. Results: A total of 300 patient records were included in the study. The Random Forest model demonstrated the highest predictive accuracy compared to Decision Tree and K-Nearest Neighbors, consistently outperforming the other models across different training-