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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

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Updated: Dec 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based

Weiming Lin^1,2, Qinquan Gao^2,3, Jiangnan Yuan^1,4

¹School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.

Frontiers in Aging Neuroscience

|April 17, 2020

Summary

Predicting Alzheimer's disease (AD) progression from mild cognitive impairment (MCI) is vital. This study uses an extreme learning machine (ELM) to fuse multimodal data, accurately identifying patients likely to convert to AD.

Keywords:

Alzheimer’s disease extreme learning machine mild cognitive impairment multimodal prediction

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Last Updated: Dec 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Area of Science:

Neuroscience
Medical Imaging
Biomarkers

Background:

Accurate prediction of Alzheimer's disease (AD) progression in patients with mild cognitive impairment (MCI) is essential for timely therapeutic intervention.
Current methods face challenges in reliably forecasting cognitive state changes from MCI to AD.

Purpose of the Study:

To develop and validate an extreme learning machine (ELM)-based grading method for predicting MCI-to-AD conversion.
To efficiently fuse multimodal data for improved prediction accuracy.

Main Methods:

Feature extraction from magnetic resonance (MR) images and subsequent feature selection.
Individual grading of multiple data modalities (MRI, PET, CSF biomarkers, gene data) using ELM.
Classification of progressive MCI from stable MCI using fused grading scores from different modalities.

Main Results:

The proposed ELM-based approach achieved 84.7% accuracy in predicting AD conversion from MCI within 3 years.
Validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort confirmed the method's efficacy.
Experiments showed consistent results for predicting AD conversion across different time frames.

Conclusions:

The multimodal data fusion approach significantly enhances the accuracy of MCI-to-AD conversion prediction.
The ELM-based grading method offers an efficient tool for identifying high-risk MCI patients for early AD intervention.