Enhanced Heart Disease Risk Prediction with Hyperparameter-Tuned Ensemble Models
Keywords:
Heart disease prediction; Ensemble learning; Machine learning algorithms; early diagnosis; Clinical decision-makingAbstract
Due to a number of risk factors, heart disease is a serious worldwide health concern that needs quick access to reliable early diagnosis and management techniques. Accurate prediction presents challenges, as seen in the limitations of traditional diagnostic methods. With the growing population, early-stage diagnosis becomes critical. Recent technological advancements have led to research in machine learning applications in healthcare, addressing these challenges. By examining pertinent variables, this work seeks to create an efficient machine learning model for the prediction of heart disease. A number of supervised learning techniques are used, such as XGBoost, K-Nearest Neighbor, Gradient Boosting, Random Forest, Decision Tree, and Logistic Regression. The primary goal is to estimate individuals' heart disease probability based on these factors. In this research, we overcome traditional diagnostic method limitations by utilizing ensemble methods, including the Gradient Boosting algorithm. This approach enhances heart disease prediction accuracy by integrating weak models. These methods open new avenues for heart disease management through detailed data analysis. The results show an impressive overall accuracy score of 99.02%. The developed model provides valuable insights, aiding informed decisions in diagnosis and treatment. Its integration into clinics supports early detection, potentially improving patient outcomes and reducing heart disease-related mortality. Beyond predictions, this study streamlines medical decision-making and revolutionizes heart disease care, enhancing patients' quality of life.
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