A preliminary study of an interpretable ensemble learning framework for atherosclerosis detection: algorithm development and exploratory feature analysis

Atherosclerotic disease detection requires models that are both accurate and clinically interpretable. This study proposes a robust and interpretable ensemble learning framework to address this need. We introduce the Progressive Saturated Exponential Loss Adaptive Boosting (ProSEL-Boost) algorithm, which enhances robustness against noisy clinical data through a dynamic loss function with a saturation mechanism. Furthermore, we develop a medical knowledge-guided framework to generate biologically meaningful interaction features based on established pathophysiological pathways, alongside a multi-strategy ensemble method for feature selection. When evaluated on a public coronary artery disease dataset (n = 303) and a proprietary atherosclerosis dataset (n = 284; 37 cases, 247 controls), ProSEL-Boost achieved superior and balanced performance (accuracy = 0.9508, AUC = 0.8807). Crucially, our interpretable feature pipeline identified the glucose-lipid interaction index (TG_FBG_Index) as a potent biomarker (Cohen’s d = 1.97, AUC = 0.86), highlighting a synergistic metabolic risk factor. Given the modest sample size, particularly the limited number of positive cases, this study should be viewed primarily as a rigorous methodological validation and hypothesis-generating investigation. The framework provides a compelling rationale for, but does not replace, validation in larger prospective cohorts, offering a transparent methodology for improving early disease detection and generating actionable insights for risk stratification.