A Stable and Scalable Digital Composite Neurocognitive Test for Early Dementia Screening Based on Machine Learning: Model Development and Validation Study

• Published in: Journal of medical Internet research 2023-12, Vol.25 (1), p.e49147-e49147
• Main Authors: Gu, Dongmei, Lv, Xiaozhen, Shi, Chuan, Zhang, Tianhong, Liu, Sha, Fan, Zili, Tu, Lihui, Zhang, Ming, Zhang, Nan, Chen, Liming, Wang, Zhijiang, Wang, Jing, Zhang, Ying, Li, Huizi, Wang, Luchun, Zhu, Jiahui, Zheng, Yaonan, Wang, Huali, Yu, Xin
• Format: Article
• Language: English
• Subjects: Aging; Algorithms; Alzheimer Disease - diagnosis; Biological markers; Biomarkers; Candidates; Clinical assessment; Cognitive ability; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - psychology; Cognitive impairment; Dementia; Differentiation; Discrimination; Education; Elimination; Feature selection; Humans; Machine Learning; Medical screening; Mental disorders; Mental Status and Dementia Tests; Missing data; Multicenter studies; Multimedia; Neurocognition; Neuroimaging; Neuropsychological Tests; Neuropsychology; Older people; Population; Primary care; Public health; Social cognition; Standard scores; Tests; Validation studies
• ISSN: 1438-8871; 1438-8871
• DOI: 10.2196/49147

Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention. Based on machine learning (ML) methods, this study aimed to develop and validate a stable and scalable panel of cognitive tests for the early detection of MCI and dementia based on the Chinese Neuropsychological Consensus Battery (CNCB) in the Chinese Neuropsychological Normative Project (CN-NORM) cohort. CN-NORM was a nationwide, multicenter study conducted in China with 871 participants, including an MCI group (n=327, 37.5%), a dementia group (n=186, 21.4%), and a cognitively normal (CN) group (n=358, 41.1%). We used the following 4 algorithms to select candidate variables: the F-score according to the SelectKBest method, the area under the curve (AUC) from logistic regression (LR), P values from the logit method, and backward stepwise elimination. Different models were constructed after considering the administration duration and complexity of combinations of various tests. Receiver operating characteristic curve and AUC metrics were used to evaluate the discriminative ability of the models via stratified sampling cross-validation and LR and support vector classification (SVC) algorithms. This model was further validated in the Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI-3) cohort (N=743), which included 416 (56%) CN subjects, 237 (31.9%) patients with MCI, and 90 (12.1%) patients with dementia. Except for social cognition, all other domains in the CNCB differed between the MCI and CN groups (P