International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Cardiovascular diseases (CVDs) are the leading cause of global mortality, as reported by the World Health Organization. Early identification of individuals at high risk is essential for effective prevention and clinical intervention. However, conventional prediction models often analyze cardiac parameters independently and fail to consider the strong correlations between metabolic disorders such as diabetes, obesity, dyslipidemia, and hypertension.This study proposes a novel deep learning-based predictive framework that integrates metabolic disorder correlations for early CVD detection. The model utilizes multi-parameter clinical data, including blood glucose, body mass index, cholesterol levels, and blood pressure, to capture complex nonlinear relationships among risk factors. A correlation-aware neural network architecture is developed to enhance predictive performance and robustness.Experimental results demonstrate improved accuracy and ROC-AUC compared to traditional machine learning approaches. The proposed framework supports early risk stratification and provides a scalable solution for preventive cardiovascular healthcare applications.

Keywords Deep learning, cardiovascular diseases (CVD), metabolic disorders, early detection, predictive modeling, artificial neural networks (ANN), risk stratification, explainable artificial intelligence (XAI).

Downloads: | DOI: 10.17148/IJARCCE.2026.15225