Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

Abstract

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

“…Further analyses are needed to determine how numerous factors, including age, sex, education, apolipoprotein-E and other relevant genetic factors, and clinical conditions relate specifically to the MemTrax parameters across numerous populations ( Bergeron et al, 2019 ; Zhou and Ashford, 2019 ). MemTrax analysis with “machine learning” can further and more definitively classify cognitive function ( Bergeron et al, 2020 ).…”

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China

“…MTx‐RT > 1.4 s 20 or MTx‐%C < 81% indicates possible cognitive impairment, according to previous study results 21 . Therefore, if MTx‐RT was longer than 1.4 s or MTx‐%C was lower than 81%, the rater would ascertain whether the subject completely understood the test instructions.…”

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

“…In the classi cation diagnosis of MCI and NC, although the accuracy rate of 97.80% is obtained using Lasso regression, only 80%-86% accuracy rate is obtained when other algorithms, including support vector machine based on feature elimination, adaptive structure learning, feature learning based on pairwise correlation are used. Bergeron et al [53] used the MemTrax test combined with the MoCA score to make model predictions and obtained a prediction accuracy of approximately 90%. Unlike the accuracy rates reported in other studies, our accuracy rate is lower; this is potentially attributed to the over tting of the XGBoost algorithm model because of the small sample size.…”