ABSTRACT Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early intervention and effective disease management. This study introduces an interpretable multi‐modal framework that integrates structural Magnetic Resonance Imaging (sMRI) features and neurocognitive clinical assessments through a hybrid deep learning and machine learning approach to predict progressive MCI (pMCI) conversion up to 3 years in advance. Cross‐sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were employed. Spatial features were extracted from sMRI scans using a three‐dimensional convolutional neural network (3D CNN), while statistically selected neurocognitive assessments constituted the clinical feature set. These features were fused and classified using machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). Model interpretability was evaluated using SHapley Additive Explanations (SHAP). The proposed multi‐modal framework achieved a classification accuracy of 88.11%, an area under the curve (AUC) of 0.889, a precision of 82.33%, a sensitivity of 90.52%, and a specificity of 78.59%. The GBM classifier demonstrated superior performance by effectively integrating imaging and clinical features. SHAP analysis identified critical biomarkers influencing prediction outcomes, and the model exhibited strong generalizability across diverse clinical sites and imaging protocols. Consequently, this study presents a robust and interpretable framework for the early prediction of MCI conversion to AD. By combining deep learning‐derived imaging features with neurocognitive assessments, the proposed method offers a promising tool to support early diagnosis and targeted intervention strategies in clinical practice.
Zarei et al. (Tue,) studied this question.