Enhancing Alzheimer's Disease Prediction with Bayesian Optimization and Ensemble Methods

The global health crisis of Alzheimer's Disease highlights the importance of early and accurate diagnosis. This study aimed to thoroughly evaluate the effectiveness of three ensemble machine learning models, namely Random Forest, AdaBoost, and XGBoost, in accurately predicting the occurrence of Alzheimer's Disease. This study holds significant importance as the timely and precise identification of Alzheimer's Disease can have a substantial influence on the provision of patient care and the implementation of effective management strategies. Multiple evaluation metrics were utilized to conduct a comprehensive evaluation of the predictive capabilities of each model. The findings of this study revealed significant insights regarding the predictive capabilities of these models. The performance of AdaBoost indicates its potential applicability in predicting Alzheimer's Disease, particularly when there is a need to strike a balanced compromise between false positives and false negatives. The ensemble models collectively performed well, with XGBoost emerging as the most notable performer, exhibiting a remarkable accuracy rate of 98.32%. It is worth mentioning that XGBoost demonstrated exceptional performance in terms of specificity (98.65%) and sensitivity (98.63%), indicating its remarkable ability to accurately classify cases of both Alzheimer's and non-Alzheimer's. This study highlights the potential of ensemble machine learning techniques, specifically XGBoost, in greatly improving the precision and dependability of Alzheimer's Disease prognosis. The timely and accurate identification of Alzheimer's Disease is crucial for the implementation of effective clinical interventions and the provision of appropriate care. The results of this study have significant implications for enhancing patient outcomes and advancing the field of medical diagnostics in the realm of neurodegenerative diseases.