Enhancing Early Detection of Cardiovascular Diseases using Machine Learning Techniques: A Comparative Study

Cardiovascular diseases (CVDs) have become a substantial worldwide health issue, resulting in elevated rates of sickness and mortality. The lack of fresh air and sedentary lifestyles have contributed to the rapid growth of CVDs. Early detection of heart diseases is crucial in preventing fatal consequences and reducing the mortality rate of patients. This work presents a comparative study on the application of various machine learning (ML) techniques, including decision tree, logistic regression, naive Bayes classifier, and ensemble learning techniques such as voting, random forest, gradient boosting, bagging, XGBoost, and AdaBoost, for the detection of heart disease. The study demonstrates that the XGBoost algorithm, a scalable tree boosting system, achieves significantly higher graded results in terms of accuracy, precision, recall, and F1-score. By utilizing these ML techniques, medical professionals can enhance their ability to detect heart diseases in the early stages, leading to more effective prevention and treatment strategies. This study adds to the growing body of evidence supporting the use of ML to enhance cardiovascular disease identification and management.