Innovative Approaches to Cardiovascular Disease: Machine Learning Predictions Unveiled & Interpretation Using LIME & SHAP

This study investigates how machine learning classification could revolutionize the field of cardiovascular disease prediction. By utilizing a variety of datasets that include demographic, lifestyle, and clinical information, several algorithms such as Logistic Regression, KNN, SVM, Decision Tree Classifier, GradientBoost, AdaBoost, XGBoost, Hyperparameter tuning, LIME and SHAP are used to create prediction models. The research hopes to facilitate early interventions and preventative measures by improving individualized risk assessments and their accuracy. The findings demonstrate the effectiveness of ML in predicting cardiovascular risks, indicating a paradigm change in the direction of preventive healthcare. With further development, incorporating these models into clinical practice may be able to reduce the prevalence of cardiovascular illnesses worldwide by implementing timely and focused therapies.