Adaptive Cardiovascular Disease Screening via Uncertainty-Guided Clinical Feature Acquisition

Cardiovascular disease (CVD) remains a major global health challenge, underscoring the need for reliable and efficient artificial intelligence-based screening strategies. Conventional CVD prediction models generally employ a fixed set of patient attributes and produce definitive classifications without explicitly considering predictive uncertainty or the resource burden associated with acquiring additional clinical information. Building on an empirical analysis of a public dataset containing 70,000 patient records, this study proposes a conceptual framework for uncertainty-guided adaptive CVD screening. The empirical foundation includes clinically informed feature engineering using body mass index (BMI), mean arterial pressure (MAP), and blood-pressure stage, together with SelectKBest Chi-Square feature selection, principal component analysis (PCA), and the evaluation of seventeen machine learning, deep learning, and CNN-based hybrid models. Multilayer Perceptron (MLP) and CNN+XGBoost achieved the highest benchmark performance, each attaining 96\% accuracy and a 0.96 F1-score. The proposed framework extends this fixed-feature paradigm by organizing clinical variables into resource-aware acquisition stages and estimating uncertainty after each stage. Additional information is acquired only when necessary, while persistently uncertain cases may trigger abstention and clinical referral. Reliability is further conceptualized through discrimination, calibration, uncertainty quality, robustness, and subgroup consistency, with future empirical validation required. This design prioritizes patient-specific information needs while minimizing unnecessary clinical measurements overall.