Transforming Heart Disease Prediction with Advanced Machine Learning Techniques

The heart disease prediction using machine learning algorithms

USING OPTIMAL MACHINE LEARNING ALGORITHMS TO PREDICT HEART FAILURE PATIENT CLASSIFICATION

Heart failure (HF) remains one of the leading causes of mortality worldwide, making early prediction and diagnosis essential for improving patient survival and reducing healthcare costs. Machine learning (ML) techniques have demonstrated considerable potential in assisting clinicians with accurate disease prediction. However, most heart failure datasets suffer from class imbalance, which negatively affects classification performance, particularly for minority class patients. This paper presents an optimized Extreme Gradient Boosting (XGBoost) model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) for heart failure patient classification. Initially, missing values, outliers, and redundant attributes are removed through preprocessing. SMOTE is then applied to balance the dataset by generating synthetic minority samples. Hyperparameter optimization using Grid Search with Stratified Cross-Validation identifies the optimal XGBoost parameters. The proposed framework is evaluated using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that the optimized XGBoost-SMOTE model significantly outperforms traditional machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, KNearest Neighbors, AdaBoost, and baseline XGBoost. The proposed approach achieves an accuracy of 98.21%, precision of 97.94%, recall of 98.47%, F1-score of 98.20%, and ROC-AUC of 99.10%, indicating superior predictive capability for heart failure diagnosis. These findings suggest that integrating SMOTE with optimized XGBoost provides an effective decision-support tool for clinical risk assessment. Similar findings have been reported in prior studies evaluating XGBoost with SMOTE-based preprocessing for heart failure prediction.

Performance Comparison of Tree-Based Models for Heart Disease Prediction Using Feature Selection and SMOTE

Heart disease remains the leading cause of mortality worldwide, highlighting the need for accurate early prediction models. This study proposes a machine learning framework for heart disease prediction using the BRFSS 2015 Heart Disease Health Indicators dataset, which contains 253,680 records and 22 attributes. The proposed approach integrates Synthetic Minority Oversampling Technique (SMOTE) for class imbalance handling, mutual information-based SelectKBest feature selection (k = 15), and three tree-based classifiers: Decision Tree, Random Forest, and XGBoost. A leakage-free preprocessing pipeline was implemented to ensure that SMOTE was applied only to the training data, and classification threshold optimization was performed to improve minority class detection. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. Experimental results show that XGBoost achieved the best performance with a cross-validation ROC-AUC of 0.9815 and a test ROC-AUC of 0.8444 at an optimized threshold of 0.20. The findings demonstrate that the proposed integration of oversampling, feature selection, and threshold optimization can improve predictive performance for imbalanced cardiovascular risk data, providing a practical foundation for machine learning–based decision support in early heart disease risk screening.  

Heart Disease Prediction Using Logistic Regression and K-Nearest Neighbor: A Comparative Study of Classification Algorithm Performance

The findings suggest that Logistic Regression is more suitable as a decision-support model for early heart disease screening due to its higher sensitivity, accuracy, and specificity.

An Advanced Ensemble Framework for Robust Heart Disease Detection and Classification

Cardiovascular Disease (CVD) remains one of the leading causes of mortality worldwide, emphasizing the need for accurate and early diagnostic solutions. Recent advances in Machine Learning (ML) and Deep Learning (DL) have shown significant potential to support clinical decision-making through data-driven prediction models. This study presents a robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component. Specifically, an Artificial Neural Network (ANN) is employed as a feature extraction layer prior to ensemble aggregation using techniques such as Random Forest, XGBoost, and LightGBM. The proposed approach is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results on a benchmark dataset demonstrate that the model achieves a high accuracy of 98.8%, outperforming individual classifiers and existing approaches. The integration of ANN-based feature extraction enhances model generalization and reduces prediction error. These findings highlight the effectiveness of the proposed framework for early heart disease detection and clinical decision support.

Artificial Intelligence for Early Heart Disease Prediction: A Review of Machine Learning Techniques

There is an urgent need for explainable, clinically validated and standardised ML frameworks to translate predictive models into routine healthcare practice and improve early detection of cardiovascular disease.