Abstract: Objective · To evaluate the reliability and validity of a computerized cognitive assessment system designed for screening mild cognitive impairment (MCI), and compare the screening accuracy among constructed different machine learning classification models. Methods · A group of random stratified samples of over 55 years old residents in the communities, nursing homes and memory-clinics Shanghai and Henan were selected to assess their cognitive status using Montreal Cognitive Assessment (MoCA)well-trained investigators. The reliability and validity were assessedintrinsic consistency analysis and factor analysis, respectively. Taking the results of MoCA as standards, four machine learning classification algorithms, i.e., na.ve Bayesian classification model, random forest classifier, Logistic regression classifier, and K-nearest neighbor classifier, were compared in accuracy and area under curve (AUC). Results · A total of 359 participants were included, the median age of whom was 63 years old. And 82.80% of them were secondary school graduates or below. According to the results of MoCA, 147 of them might be MCI. The Cronbachs α and KMO of this system were 0.84 and 0.78, respectively; Bartletts sphericity test was significant (P<0.05); thirteen common factors could explain 75.10% of the system. The best classification model was na.ve Bayesian classification model, and its accuracy andAUC were 88.05% and 0.941, respectively. Conclusion · The new designed computerized cognitive assessment system has been proved to be reliable and valid. The na.ve Bayesian classification model has good classification accuracy.

Key words: mild cognitive impairment (MCI), computerized cognitive assessment system, machine learning, na.ve Bayesian classification model, Montreal Cognitive Assessment (MoCA), screening