Diagnosis of Alzheimer's Disease Using Boosting Classification Algorithms
Mithat Önder, Ümit Şentürk, Kemal Polat, D. Paulraj
Abstract
Alzheimer's Disease (AD) is a progressive degenerative disorder of the brain that impacts memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently no cure for Alzheimer's Disease. However, there are available treatments to manage symptoms and slow their advancement. This research conducted a comprehensive study to diagnose AD using four different categorization methods. These methods included XGBoost, GradientBoost, AdaBoost, and voting classification algorithms. To carry out the examination, a high-quality dataset was obtained from the collection of machine learning data of the prestigious University of California. This dataset was carefully selected to ensure accurate and reliable results. The analysis of the collected data revealed some interesting findings. XGBoost exhibited an accuracy rate of 85% in diagnosing Alzheimer's Disease. ADABoost also performed, achieving an accuracy rate of 75%. GradientBoost, similarly, obtained an accuracy rate of 85%.Additionally, the voting classification algorithms showed promise, attaining an accuracy rate of 80%. All these accuracy rates were obtained by implementing a 5-fold cross-validation methodology, which ensured robust and unbiased results. This research contributes to the field of AD diagnosis by providing insights into the effectiveness of different categorization methods.