Abstract
Early detecting cognitive symptoms and determination of cognitive status among patients with Alzheimer's disease (AD) are important works. Recent research has demonstrated the potential values in facilitating the early detection of AD. However, very few studies investigated the values of performance on neuropsychological assessment in the detection and determination of cognitive status among AD patients with machine learning (ML) algorithms. The present study compared the discriminative abilities of neuropsychological functions and demographic and clinical features among patients using ML and traditional logistic regression approaches among 198 individuals comprising a cognitively unimpaired group (CU, n = 30), a mild cognitive impairment group (MCI, n = 125), and a group of patients with mild dementia of Alzheimer's type (DAT, n = 43). Results revealed, in general, the ML method outperformed the traditional logistic regression model in terms of accuracy and area under the operating characteristic curve. Furthermore, the ML method is better than the traditional logistic regression model in classifying CU and MCI, while the performance of the ML method is similar to the traditional logistic regression model in classifying DAT. To the best of our knowledge, this is the first study incorporated ML and neuropsychological function assessment in Taiwan. ML algorithms might have values in facilitating the early detection and differentiation of cognitive symptoms among patients with AD.