Modelling the risk of conversion from mild cognitive impairment to Alzheimer's disease using machine learning.
- 1Euromed Research Center, Euromed University of Fez (UEMF), Fez, Morocco.
- 2Department of Biology, PEDSM Laboratory, CRMEF Institute, Fez, Morocco.
Abstract
Background
Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia, characterized by subtle cognitive decline. While not all individuals with MCI will progress to Alzheimer's disease (AD), early identification of those at high risk is crucial for timely interventions. Machine learning algorithms offer a promising approach for analyzing complex datasets, including neuroimaging and cognitive assessments, to predict the likelihood of progression to AD. In this study, we evaluate 12 Machine Learning algorithms to predict the risk of progression from MCI to AD and identify the most important features contributing to this progression.Materials and methods
We selected 899 MCI subjects (596 stable, 303 progressive) from the AD Neuroimaging Initiative dataset. ML algorithms were used to predict the progression based on predictive features, including cognitive measures, demographic variables, and neuroimaging biomarkers.Results
Linear Discriminant Analysis was identified as the top-performing model, achieving an accuracy of 73.43%, an area under the curve of 0.78, a recall of 69.53%, a precision of 59.38%, and an F1-score of 63.91%. These results demonstrate the model's ability to effectively identify individuals at risk of progressing to AD. Hippocampal volume and the AD Assessment Scale-Cognitive were identified as the most influential predictors of conversion.Conclusion
The methodology can be generalized to other types of disease progression, such as transitions from healthy control to MCI or AD. Future studies could incorporate additional biomarkers and longitudinal data to enhance predictive accuracy and disease prevention.
Related
- Joint modeling approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease: A systematic review (1999-2025).
- Identifying Mild Cognitive Impairment from Routinely Collected Physical, Functional and Psychosocial Measures in Community-Dwelling Older Adults: A Comparative Machine-Learning and Regression Modelling Study in the Northern Japanese ORANGE Registry.
- Systematic Modality Ablation of Multimodal Machine Learning for Predicting 24-Month Progression from Mild Cognitive Impairment to Alzheimer's Disease
- Recognising Mild Cognitive Impairment and Early Alzheimer's Disease in General and Psychiatric Practice: Clinical Clues and Appropriate Referral
- Sex‐specific brain reserve and progression from mild cognitive impairment to Alzheimer's disease dementia
BibTeX
@article{mouiha2026modelling,
title = {{Modelling the risk of conversion from mild cognitive impairment to Alzheimer's disease using machine learning.}},
author = {Abderazzak Mouiha and Mohamed Taiebine and Jaouad El Hilaly},
journal = {Journal of education and health promotion},
year = {2026},
month = sep,
doi = {10.4103/jehp.jehp_1279_25},
url = {https://europepmc.org/article/MED/42924292},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Modelling the risk of conversion from mild cognitive impairment to Alzheimer's disease using machine learning.
AU - Abderazzak Mouiha
AU - Mohamed Taiebine
AU - Jaouad El Hilaly
PY - 2026
DA - 2026/09/30
JO - Journal of education and health promotion
DO - 10.4103/jehp.jehp_1279_25
UR - https://europepmc.org/article/MED/42924292
AB - Background Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia, characterized by subtle cognitive decline. While not all individuals with MCI will progress to Alzheimer's disease (AD), early identification of those at high risk is crucial for timely interventions. Machine learning algorithms offer a promising approach for analyzing complex datasets, including neuroimaging and cognitive assessments, to predict the likelihood of progression to AD. In this study, we evaluate 12 Machine Learning algorithms to predict the risk of progression from MCI to AD and identify the most important features contributing to this progression.Materials and methods We selected 899 MCI subjects (596 stable, 303 progressive) from the AD Neuroimaging Initiative dataset. ML algorithms were used to predict the progression based on predictive features, including cognitive measures, demographic variables, and neuroimaging biomarkers.Results Linear Discriminant Analysis was identified as the top-performing model, achieving an accuracy of 73.43%, an area under the curve of 0.78, a recall of 69.53%, a precision of 59.38%, and an F1-score of 63.91%. These results demonstrate the model's ability to effectively identify individuals at risk of progressing to AD. Hippocampal volume and the AD Assessment Scale-Cognitive were identified as the most influential predictors of conversion.Conclusion The methodology can be generalized to other types of disease progression, such as transitions from healthy control to MCI or AD. Future studies could incorporate additional biomarkers and longitudinal data to enhance predictive accuracy and disease prevention.
SN - 2277-9531
SN - 2319-6440
ER -