Cardiovascular diseases prediction using machine learning algorithms: A comparative study.

Cardiovascular Diseases (CVD) have become increasingly common around the world in recent times. Diagnosing these diseases is difficult due to traditional diagnostic methods such as stethoscopes and auscultation. This study aimed to detect and predict these diseases before the patient's condition worsens. Machine learning (ML) techniques were used, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Decision Tree (DT). A Kaggle dataset of 1,000 samples and 14 clinical features was trained and tested. Furthermore, the performance metrics of these algorithms were examined. According to the results, RF performs better in terms of F1-score, accuracy, sensitivity, specificity, precision, training time, and testing time, with scores of 97.8%, 98%, 98.2%, 97.6%, 97.8%, 0.182 sec, and 0.030 sec, respectively. In conclusion, it was noted that modern diagnostic methods provide comprehensive and exact information about the heart. [ABSTRACT FROM AUTHOR]