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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

An explainable machine learning approach for Alzheimer's disease classification.

Abbas Saad Alatrany^1,2,3,4, Wasiq Khan⁵, Abir Hussain^6,7

¹School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK. a.s.alatrany@2020.ljmu.ac.uk.

Scientific Reports

|February 1, 2024

Summary

This summary is machine-generated.

Machine learning models accurately predict Alzheimer's disease (AD) risk and progression using National Alzheimer's Coordinating Center data. Explainable AI methods identify key factors like memory and judgment, aiding early diagnosis and understanding of AD development.

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Area of Science:

Neurology
Computational Biology
Biostatistics

Background:

Early Alzheimer's disease (AD) diagnosis is challenging due to subtle biomarker changes.
Machine learning (ML) offers potential for identifying AD risk but often lacks explainability.
High dimensionality and limited datasets pose challenges in AD research.

Purpose of the Study:

To develop and validate explainable ML models for early AD diagnosis and progression prediction.
To identify key factors contributing to AD development using interpretable ML approaches.
To leverage a large-scale dataset for robust AD risk assessment.

Main Methods:

Utilized a National Alzheimer's Coordinating Center dataset (169,408 records, 1024 features) with feature space reduction.
Trained Support Vector Machine (SVM) models for binary (NC vs. AD) and multiclass classification and progression prediction.
Employed rule-extraction techniques (class rule mining, stable and interpretable rule set) and SHAP/LIME for model explainability.

Main Results:

SVM models achieved high performance: 98.9% F1 score for binary classification and 90.7% for multiclass.
SVM accurately predicted AD progression (88% F1 for binary, 72.8% for multiclass).
Explainable AI identified critical factors: MEMORY, JUDGMENT, COMMUN, ORIENT, and the Clinical Dementia Rating tool.

Conclusions:

Explainable ML models demonstrate high accuracy in AD diagnosis and progression prediction.
Identified key cognitive and clinical factors crucial for understanding AD development.
The study highlights the utility of interpretable ML in clinical decision support for Alzheimer's disease.