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Updated: Feb 16, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical
J Ramírez1, J M Górriz2, A Ortiz3
1Dept. of Signal Theory, Networking and Communications, University of Granada, Spain.
Background:
Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments.
Method:
The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level.
Results:
The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects.
Comparison With Existing Method(S):
The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning.
Conclusions:
A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set.
Insights
This study presents a novel machine learning system for predicting Alzheimer's disease (AD) progression from Mild Cognitive Impairment (MCI) using MRI data. The system achieved 56.25% accuracy, demonstrating its potential for early AD detection.
Area of Science:
- Neuroimaging
- Machine Learning
- Biomedical Engineering
Background:
- Alzheimer's disease (AD) is a leading cause of dementia in older adults, affecting millions globally.
- Mild cognitive impairment (MCI) often precedes AD, with 10-15% of individuals progressing annually.
- Early prediction of AD from MCI using MRI is crucial for effective treatment.
Purpose of the Study:
- To develop and evaluate a machine learning system for predicting Alzheimer's disease (AD) progression from Mild Cognitive Impairment (MCI) using MRI data.
- To enhance the accuracy of classifying healthy controls (HC) from MCI subjects.
- To compare the proposed system's performance against various classification strategies and base classifiers.
Main Methods:
- The system utilizes feature standardization, ANOVA feature selection, and Partial Least Squares (PLS) dimension reduction.
- An ensemble of One vs. Rest (OvR) random forest classifiers is employed.
- A two-level classification approach is introduced to improve HC vs. MCI discrimination.
Main Results:
- The system was trained and validated on Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, including T1-weighted MRI data.
- The proposed system achieved a 56.25% classification accuracy on a test set of 160 real subjects.
- The classifier outperformed One vs. One (OvO), OvR, Error Correcting Output Codes (ECOC), Support Vector Machines (SVM), Gradient Boosting, and Random Forest base classifiers.
Conclusions:
- A robust method for MCI prediction using MRI data was developed for an international challenge.
- The system achieved the second-best performance in the competition, with a 56.25% accuracy rate on real test subjects.
- This approach shows promise for improving early detection and intervention strategies for Alzheimer's disease.
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