Enhancing Cardiovascular Health through Gradient Boosting Classifier-Based Risk Prediction

Predicting heart disease is an important field of medical research that seeks to find those who are at risk and offer prompt care. Because of its better performance in robustness and predictive accuracy among machine learning algorithms, the Gradient Boosting Classifier (GBC) has become a top model. In order to assess the GBC's predictive power against other widely used models such Logistic Regression, Support Vector Machines, and Neural Networks, this work compares them. A large dataset including age, blood pressure, cholesterol, and electrocardiographic findings is used in the study. Results show that the GBC beats other models in terms of F1-score, accuracy, precision, and recall. GBC is a very trustworthy model for heart disease prediction since of its intrinsic capacity to manage complicated non-linear interactions and its efficiency in reducing overfitting by boosting methods. These results emphasize the Gradient Boosting Classifier's promise as a useful instrument in clinical settings for the early identification and treatment of heart disease.