The Linear Discriminant Analysis classifier achieved 100% accuracy distinguishing healthy controls from Alzheimer's patients, indicating its potential effectiveness.
Multi-class classification yielded 84.67% accuracy for differentiating depression, MCI, and schizophrenia among the participants in the EEG dataset of 40 Alzheimer's patients and 43 controls.
Various feature extraction methods were applied, using the Lasso algorithm for feature selection to enhance classification performance across multiple neurological disorders.
This research emphasizes disease-to-disease classification, potentially leading to more effective diagnostic tools in clinical environments.
Cite This Study
Çiğdem Gülüzar Altıntop (2025) studied this question.