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Updated: Jul 16, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time
Yafei Wu1,2, Xing Wang1,2, Chenming Gu1,2
1School of Public Health, Xiamen University, Xiamen, Fujian, China.
Background:
Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations.
Methods:
Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance.
Results:
The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors.
Conclusion:
Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.
Insights
Machine learning models accurately predict Alzheimer's disease progression in Mild Cognitive Impairment patients. This aids early screening and intervention for those at high risk.
Area of Science:
- Neurology
- Artificial Intelligence
- Biomarkers
Background:
- Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's Disease (AD).
- Approximately 10-12% of MCI patients progress to AD annually.
- Effective early diagnostic markers for MCI to AD progression are currently lacking.
Purpose of the Study:
- To develop machine learning models for predicting MCI to AD progression within 3 years.
- To aid in the screening and prevention of high-risk populations.
- To improve early diagnosis and intervention strategies for AD.
Main Methods:
- Utilized data from the Alzheimer's Disease Neuroimaging Initiative.
- Applied machine learning models to predict MCI to AD progression.
- Incorporated demographic, neuropsychological, and MRI-related biomarkers.
- Evaluated models using AUC (Area Under the ROC Curve).
Main Results:
- The AdaBoost model, based on logistic regression, achieved the highest performance (AUC: 0.98) for 0-6 month predictions.
- Key predictors included scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test, and ADAS11.
- Machine learning models demonstrated high accuracy in predicting MCI to AD progression.
Conclusions:
- Machine learning, combined with neuropsychological and MRI markers, accurately predicts MCI to AD progression, particularly in the short term.
- This approach is significant for clinical screening, diagnosis, and early intervention in high-risk MCI patients.
- Early identification and treatment can significantly impact patient outcomes.
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