Development of AI based behavioral feature patterns on influencing asymptomatic cardiovascular disease attributes: a...

Cardiovascular diseases (CVD) remain a major global health challenge, characterized by heterogeneous clinical attributes and multi-source data variability that limit reliable predictive modeling. Existing approaches primarily focus on independent feature analysis, often overlooking the interdependency between symptomatic and asymptomatic attributes. In this study, a transfer learning-based framework is proposed for cross-domain feature harmonization and interdependency-driven attribute modeling. Multi-source datasets, including MIMIC-III, Framingham Heart Study (FHS), and Cleveland Heart Disease (CHD), are integrated to construct a unified latent feature space. The proposed method systematically captures behavioral interdependencies between clinical attributes and introduces a confidence-calibrated influence scoring mechanism to enhance interpretability and reliability of predictions. Experimental results demonstrate improved performance, achieving a sensitivity of 92.37% and an F1-score of 0.8962. The findings highlight the effectiveness of interdependency-aware feature mapping in improving CVD risk characterization and provide a standardized framework for robust and explainable clinical decision support.