PredictHeart-X: A Novel Explainable Deep Learning Framework for Cardiovascular Disease Prediction

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, necessitating the development of accurate and intelligent predictive systems for early diagnosis and prevention. This research proposes PredictHeart-X, a novel explainable deep learning framework designed for effective cardiovascular disease prediction using clinical and patient health parameters. The proposed model integrates advanced deep learning techniques with explainable artificial intelligence (XAI) mechanisms to enhance prediction accuracy while maintaining interpretability for healthcare professionals. The framework employs a hybrid architecture combining Deep Neural Networks (DNN) and Attention-based learning mechanisms to analyze complex relationships among medical attributes such as age, blood pressure, cholesterol levels, heart rate, electrocardiogram results, and other cardiovascular indicators. Data preprocessing techniques including normalization, missing value handling, and feature optimization are applied to improve model performance and reliability. To address the black-box nature of deep learning, explainability methods such as SHAP and attention visualization are incorporated to identify the most influential clinical features contributing to disease prediction. Experimental evaluation is conducted using benchmark cardiovascular datasets, and the proposed PredictHeart-X model is compared with traditional machine learning algorithms and existing deep learning approaches. The results demonstrate superior prediction accuracy, precision, recall, F1-score, and reduced false prediction rates. The explainable component further assists clinicians in understanding the decision-making process of the model, thereby increasing trust and usability in real-world healthcare applications. The proposed system offers an efficient, scalable, and interpretable solution for early cardiovascular disease detection, supporting healthcare practitioners in making timely and informed medical decisions while improving patient outcomes.