Key result
Random Forest predicts heart disease with ~92% accuracy, outperforming other machine learning models.
Why the study?
Heart disease is a leading cause of death worldwide, making early prediction essential for improving patient outcomes, and machine learning techniques have gained attention for analyzing clinical data to predict risk.
Do ensemble machine learning algorithms and data preprocessing techniques improve the accuracy of early cardiovascular disease risk prediction compared to other classification models?
Do ensemble machine learning algorithms and data preprocessing techniques improve the accuracy of early cardiovascular disease risk prediction compared to other classification models?
This survey and system architecture proposal highlights that ensemble machine learning algorithms like XGBoost and Gradient Boosting, combined with feature selection and SMOTE class balancing, offer high accuracy for early cardiovascular disease risk prediction.
Ensemble ML may improve early heart disease prediction accuracy; leaves open prospective validation before clinical adoption.
Heart disease is one of the leading causes of death worldwide, making early prediction essential for improving patient outcomes.Machine learning techniques have gained significant attention for predicting heart disease by analyzing medical and clinical data.This survey paper reviews recent studies on heart disease prediction using various machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost.It also discusses feature selection techniques, data preprocessing methods such as SMOTE, and model optimization approaches that improve prediction performance.Based on the reviewed literature, ensemble learning methods, particularly XGBoost and Gradient Boosting, demonstrate superior accuracy and reliability.This survey highlights the strengths, limitations, and future scope of machine learning-based heart disease prediction systems and emphasizes their potential to support early diagnosis and clinical decision-making.
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H et al. (2026) studied Cardiovascular disease / Heart disease. Random Forest and Convolutional Neural Network (CNN) machine learning framework vs. Alternative machine learning classifiers and cross-validation splits was evaluated on Test classification accuracy for heart disease prediction. Random Forest achieved 91.80% test accuracy (82.63% 5-fold cross-validation) for clinical heart disease prediction, while a CNN achieved 85.88% test accuracy for 4-class ECG classification.
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