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. 2025;45(1):63-83.
doi: 10.1007/s40846-024-00918-z. Epub 2024 Dec 24.

Machine Learning Approaches for Predicting Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment

Fatih Gelir  1 , Taymaz Akan  1 , Sait Alp  2 , Emrah Gecili  3   4 , Md Shenuarin Bhuiyan  5   6 , Elizabeth A Disbrow  7   8   9   10 , Steven A Conrad  1   11 , John A Vanchiere  11 , Christopher G Kevil  5   6 ; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Mohammad Alfrad Nobel Bhuiyan  1   5   8
Affiliations

Machine Learning Approaches for Predicting Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment

Fatih Gelir et al. J Med Biol Eng. 2025.

Abstract

Purpose: Alzheimer's disease (AD), a neurodegenerative disorder, is a condition that impairs cognition, memory, and behavior. Mild cognitive impairment (MCI), a transitional stage before AD, urgently needs the development of prediction models for conversion from MCI to AD.

Method: This study used machine learning methods to predict whether MCI subjects would develop AD, highlighting the importance of biomarkers (biological indicators from neuroimaging, such as MRI and PET scans, and molecular assays from cerebrospinal fluid or blood) and non-biomarker features in AD research and clinical practice. These indicators aid in early diagnosis, disease monitoring, and the development of potential treatments for MCI subjects. Using baseline data, which includes measurements of different biomarkers, we predicted disease progression at the patient's last visit. The Shapley value explanation (SHAP) technique was used to identify key features for predicting patient progression.

Results: The study used the ADNI database to evaluate the effectiveness of eight classification methods for predicting progression from MCI to AD. Four fundamental data sampling approaches were compared to balance the dataset and reduce overfitting. The SHAP technique improved the ability to identify biomarkers and non-biomarker features, enhancing the prediction of disease progression. NEAR-MISS was found to be the most advantageous sampling method, while XGBoost was found to be the superior classification method, offering enhanced accuracy and predictive power.

Conclusion: The proposed SHAP for feature selection combined with XGBoost may provide improved predictive accuracy in diagnosing Alzheimer's patients.

Keywords: Alzheimer's disease; Balancing; Feature selection; Machine learning; Shapley value explanation technique.

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Conflict of interest statement

Conflict of interestThe remaining authors have nothing to disclose.

Figures

Fig. 1
Fig. 1
Comparative visualization of feature importance across SHAP a, XGBoost b, and the Gini index c methods
Fig. 1
Fig. 1
Comparative visualization of feature importance across SHAP a, XGBoost b, and the Gini index c methods
Fig. 2
Fig. 2
The correlation matrix of the union of features selected by three different feature selection methods (SHAP, XGBoost, and the Gini index) is used to identify key biomarkers for predicting the transition from MCI to AD
Fig. 3
Fig. 3
Performance of ML classifiers by feature selection method: AUC scores for SHAP, XGBOOST, GINI-INDEX, and Common Features
Fig. 4
Fig. 4
Performance of ML classifiers by feature selection method: APR scores for SHAP, XGBOOST, GINI-INDEX, and Common Features
Fig. 5
Fig. 5
ROC curve analysis highlighting ML model performances for predicting progression from sMCI to pMCI
Fig. 6
Fig. 6
Precision-recall performance of ML models using multiple feature selection methods for progression from sMCI to pMCI
See this image and copyright information in PMC

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