Article
EARLY DETECTION AND PREDICTION OF HEART DISEASE RISK USING DEEP LEARNING
Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient predictive systems for early diagnosis and risk assessment. Traditional diagnostic approaches often rely on clinical expertise and manual evaluation of multiple patient parameters, which may be time-consuming and prone to variability. Recent advances in artificial intelligence, particularly deep learning, have demonstrated significant potential in healthcare analytics by enabling automated extraction of complex patterns from medical data. This study presents a deep learning-based framework for the early detection and prediction of heart disease risk using patient health records and clinical attributes. The proposed model utilizes multiple cardiovascular risk indicators, including age, blood pressure, cholesterol levels, blood glucose, heart rate, electrocardiographic measurements, and other relevant clinical factors to predict the likelihood of heart disease occurrence. The developed framework incorporates data preprocessing, feature normalization, and deep neural network architecture to improve prediction accuracy and model generalization. The model is trained and evaluated on a benchmark cardiovascular dataset, where its performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrate that the proposed deep learning model outperforms several conventional machine learning techniques in identifying high-risk individuals. The system effectively captures complex nonlinear relationships among clinical variables, leading to enhanced diagnostic reliability and reduced false predictions. The findings indicate that deep learning can serve as a valuable decision-support tool for healthcare professionals, facilitating timely intervention and personalized treatment planning. The proposed approach contributes to the advancement of intelligent healthcare systems by enabling early risk identification, improving patient outcomes, and supporting preventive cardiovascular care.
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