Classifying Alzheimer’s disease using machine learning: Insights from default mode network alterations

Abstract

Alzheimer’s disease (AD) is a brain disorder that can be fatal and is marked by a progressive loss of cognitive function. It has become a global health concern and is the most frequent type of dementia in the elderly. Although there is currently no effective treatment, there are medications that can halt its progression. For this reason, identification of AD is vital for controlling and limiting the progression of the illness. Here, a machine-learning approach is suggested for detecting AD by examining the alterations in the Default Mode Network (DMN) functional connections. The study utilizes fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We extract time-series signals from 11 voxel regions of the DMN, compute functional connectivity using both Pearson correlation and instantaneous phase synchronization, and train various classifiers. A 10-fold cross-validation strategy was employed to ensure robustness and generalizability. Among the classifiers, the linear SVM model achieved the best performance, with an accuracy of 93.33%, sensitivity of 95.56%, and specificity of 91.11% on 10-fold cross-validation. These results outperform prior DMN-based approaches and demonstrate the utility of dynamic synchronization features in early AD diagnosis.