Ensemble Learning-Based Alzheimer’s Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to achieve better predictive performance than a single learning algorithm alone. Machine learning techniques, including EL, have been applied in the field of medicine to assist in the clinical interpretation of specific diseases. Although neurodegenerative diseases, especially Alzheimer's disease (AD), are of interest to clinicians and researchers due to their rapid increase in clinical cases, the application of EL in AD diagnosis has been relatively less attempted. In this research, we demonstrate that three machine learning algorithms, trained on an ensemble of electroencephalogram (EEG) and clock drawing test (CDT) feature data for an AD classification task, show improved AD detection accuracy compared to when either the EEG or CDT dataset is used independently. We also explore which feature contributes most to decision-making in AD and healthy control (HC) classification. In conclusion, the current study suggests that EL can be a novel clinical application of machine learning (ML) in the automated AD screening process.