Research on interpretable machine learning models for diagnosis and staging of mild cognitive impairment.

Background: Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), further categorized into early MCI (EMCI) and late MCI (LMCI). Early and accurate diagnosis is essential for effective prevention and intervention of AD. This study aims to develop an accessible and interpretable machine learning model to facilitate early diagnosis and subtype staging of MCI. Methods: A total of 268 participants were recruited from the ADNI, including cognitively normal individuals (CN, Results: The combined model significantly outperformed unimodal models across all metrics, achieving macro_AUC = 0.92, micro_AUC = 0.91, and ACC = 0.81 in the training set, and macro_AUC = 0.87, micro_AUC = 0.87, and ACC = 0.76 in the testing set. The LR-based radiomics model ranked second. Models based solely on clinical neuropsychological scales or plasma protein biomarkers demonstrated comparatively lower classification performance. SHAP analysis highlighted neuropsychological scales (ADAS-Cog, MoCA) and radiomic features from critical brain regions (hippocampus, middle temporal gyrus, entorhinal cortex) as pivotal contributors to model efficacy. Conclusion: The integration of whole-brain structural MRI (sMRI) radiomics, neuropsychological scales, and plasma protein biomarkers significantly improves the precision of diagnosing and staging mild cognitive impairment (MCI). Radiomic characteristics derived from critical cerebral regions yield valuable pathological information that facilitates clinical interpretation. This methodology presents a promising strategy for the early identification and individualized management of MCI.