Random Forest and XGBoost ensemble models demonstrated the highest accuracy and reliability for early heart disease prediction compared to Logistic Regression, SVM, and KNN.
Machine learning ensemble models, specifically Random Forest and XGBoost, show strong potential for accurate early prediction of heart disease risk using standard clinical parameters.
Estimación del efecto: Highest accuracy with ensemble models (Random Forest and XGBoost) compared to others
Heart disease continues to be a major global health concern, accounting for a significant number of premature deaths each year. Early detection can improve survival rates, yet traditional diagnostic methods are time-consuming and often dependent on expert interpretation. This study applies machine learning techniques to clinical data to develop a predictive model capable of estimating heart disease risk. Various algorithms—including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—were evaluated. The results show that ensemble models deliver the highest accuracy, demonstrating strong potential for supporting clinical decision-making.
Jaiswal et al. (Thu,) conducted a other in Patients at risk of heart disease based on clinical parameters including age, gender, blood pressure, cholesterol levels, fasting blood sugar, ECG results, chest pain type, and physical exercise responses. Random Forest and XGBoost ensemble machine learning algorithms vs. Other machine learning models including Logistic Regression, SVM, KNN was evaluated on Accuracy of heart disease risk prediction by machine learning models (Highest accuracy with ensemble models (Random Forest and XGBoost) compared to others). Random Forest and XGBoost ensemble models demonstrated the highest accuracy and reliability for early heart disease prediction compared to Logistic Regression, SVM, and KNN.