What question did this study set out to answer?

This research aims to accurately predict the transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using a novel framework.

June 12, 2026

Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model

Key Points

This research aims to accurately predict the transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using a novel framework.
Utilized a hybrid deep learning and machine learning model integrating structural MRI features and neurocognitive assessments.
Extracted spatial features from MRI scans using a 3D convolutional neural network.
Employed various machine learning classifiers including RF, LR, SVM, and GBM for prediction, with model interpretability evaluated via SHAP.
Achieved a classification accuracy of 88.11% with an area under the curve (AUC) of 0.889.
The GBM classifier outperformed other models, demonstrating effective integration of imaging and clinical features.
Identified critical biomarkers influencing prediction outcomes with strong generalizability across different clinical sites.

Abstract

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.

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