Gradient-Based Deep Learning Framework for Early and Accurate Heart Disease Diagnosis
Keywords:
Heart Disease Prediction, Gradient-Driven Convolutional Network (GDCN), Convolutional Neural Network (CNN), XGBoost, Machine Learning in Healthcare, Medical Data Analysis, Cleveland Heart Disease Dataset, Cardiovascular Disease DatasetAbstract
Early detection of heart disease is vital for timely intervention and improved patient outcomes, as it remains a major global cause of death. Traditional machine learning models like Extreme Gradient Boosting (XGBoost) and Autoencoders have been widely applied to medical datasets but face challenges in either feature extraction or classification accuracy when used independently. This paper presents the Gradient-Driven Convolutional Network (GDCN). The hybrid model merges the spatial pattern recognition strength of Convolutional Neural Networks (CNN) with the decision-making efficiency of XGBoost. The model is evaluated on the Cleveland Heart Disease Dataset and the Cardiovascular Disease Dataset using comprehensive preprocessing and key performance metrics, including Accuracy, Precision, Recall, Specificity, F1 Score, AUC-ROC, and AUC-PR. GDCN provides improved prediction accuracy compared to existing methods, such as XGBoost, Autoencoders, DBANP, and DSLS.
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