A Stacking-based Ensemble Learning Model with Genetic Algorithm For detecting Early Stages of Alzheimer’s Disease

Alzheimer's disease (AD) affects fifty million people worldwide and is the sixth cause of death in the United States. However, there is no cure or treatment for patients with AD; thus, it is important to detect this disease at an early stage to improve patients' lives qualities. Several studies have been proposed to detect and differentiate between different AD groups, although most of these works only focused on differentiating between healthy people and people with Alzheimer's. These studies also did not identify the most reliable biomarkers to provide more accurate results and did not use the best hyperparameters to provide optimal results. To address these issues, we developed a model that leads to a better performance in differentiating between healthy people (cognitively normal), people with mild cognitive impairment, and people with Alzheimer’s disease. For this purpose, we combined a stacking-based ensemble learning, consisting of four traditional classifiers, with a hyperparameter tuning technique, a genetic algorithm. The model was evaluated in terms of accuracy, precision, recall, and F1-score. The simulation results show that stacking-based ensemble learning, using genetic algorithm, provides 96.7% accuracy, 96.5% recall, 97.9% precision, and 97.1% F1-score in differentiating between CN, MCI, and AD groups.