Alzheimer’s Disease Detection Utilising Ensemble Learning Approaches Based on Structural Biomarkers

Researchers have developed a powerful new method to detect Alzheimer's disease (AD) by combining brain MRI scans with advanced artificial intelligence techniques. The study used sophisticated computer algorithms to analyse structural changes in the brain, particularly in regions such as the entorhinal cortex, parahippocampal area, and inferior temporal regions, which are known to be affected early in AD progression. By combining logistic regression and a support vector machine (SVM) in an ensemble learning (EL) approach, the researchers achieved remarkably high accuracy rates of 99% for distinguishing between healthy individuals and those with AD, 96% for detecting mild cognitive impairment (MCI), and 85% for identifying progression from MCI to AD. When the model was evaluated for multiclass classification distinguishing healthy controls, individuals with MCI, and patients with AD, it achieved an overall accuracy of 93%, demonstrating strong generalisation across all diagnostic categories. This reflects the efficacy of integrating structural MRI features with EL-based machine learning (ML) techniques, yielding a robust and interpretable diagnostic framework. Such an approach holds significant clinical promise, as it supports early and reliable detection of AD, potentially facilitating timely intervention and improved patient management.