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

TL;DR

This study develops an interpretable machine learning model combining brain scans, cognitive tests, and blood biomarkers to improve early diagnosis and staging of mild cognitive impairment.

Contribution

The novel contribution is an interpretable machine learning model integrating multimodal data for MCI diagnosis and staging with SHAP-based feature interpretation.

Findings

The combined model achieved macro_AUC of 0.92 in training and 0.87 in testing for MCI classification.

SHAP analysis identified key features like ADAS-Cog, MoCA, and hippocampal radiomics as critical for model performance.

Radiomics-based models outperformed models using only clinical or plasma biomarker data.

Abstract

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.

A total of 268 participants were recruited from the ADNI, including cognitively normal individuals (CN, n = 132), EMCI (n = 95), and LMCI (n = 41). Participants were randomly divided into training (80%) and testing (20%) cohorts. Multimodal data encompassing whole-brain T1-WI MRI radiomics, clinical neuropsychological scales and plasma protein biomarkers were collected. Logistic regression (LR) and random forest (RF) algorithms were employed to construct six unimodal models based on above three categories of features, as well as a combined model combining all features. Diagnostic performance for the three-class classification task (CN, EMCI, LMCI) was evaluated using receiver operating characteristic (ROC) curve. Furthermore, SHapley Additive exPlanations (SHAP) were applied to quantify the contribution of individual features within the integrated model.

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.

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.

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