Enhancing Early Dementia Detection: A Machine Learning Approach Leveraging Cognitive and Neuroimaging Features for Optimal Predictive Performance

Abstract

Dementia, including Alzheimer’s Disease (AD), is a complex condition, and early detection remains a formidable challenge due to limited patient records and uncertainty in identifying relevant features. This paper proposes a machine learning approach to address this issue, utilizing cognitive and neuroimaging features for training predictive models. This study highlighted the viability of cognitive test scores in dementia detection—a procedure that offers the advantage of simplicity. The AdaBoost Ensemble model, trained on cognitive features, displayed a robust performance with an accuracy rate of approximately 83%. Notably, this model surpassed benchmark models such as the Artificial Neural Network, Support Vector Machine, and Naïve Bayes. This study underscores the potential of cognitive tests and machine learning for early dementia detection.