Analysis and Enhancement of Prediction of Cardiovascular Disease Diagnosis using Machine Learning Models SVM, SGD, and XGBoost

Cardiovascular disease (CVD), claiming 17.9 million lives annually, is exacerbated by factors like high blood pressure and obesity, prompting extensive data collection for deeper insights. Machine learning aids in accurate diagnosis, with techniques like SVM, SGD, and XGBoost proposed for heart disease prediction, addressing challenges such as data imbalance and optimizing diagnostic accuracy. This study integrates these algorithms to improve cardiovascular disease diagnosis, aiming to reduce mortality rates through timely interventions. This research investigates the efficacy of Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and XGBoost machine learning techniques for heart disease prediction. Analysis of the models' performance metrics reveals distinct characteristics and capabilities. SVM demonstrates robust performance with a training accuracy of 88.28% and a model accuracy score of 87.5%, exhibiting high precision and recall values across both classes. SGD, while commendable with a training accuracy of 83.65% and a model accuracy score of 84.24%, falls slightly behind SVM in accuracy and precision. XGBoost Classifier showcases perfect training accuracy but potential overfitting, yet demonstrates comparable precision and recall values to SVM. Overall, SVM emerges as the most effective model for heart disease prediction, followed by SGD and XGBoost Classifier. Further optimization and investigation into generalization capabilities are recommended to enhance the performance of SGD and XGBoost Classifier in clinical settings.