Machine learning-based estimation of the mild cognitive impairment stage using multimodal physical and behavioral...

Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes. However, current diagnostic tools such as brain magnetic resonance imaging (MRI) and neuropsychological testing have limited accessibility and scalability. Using machine-learning models, we aimed to evaluate whether multimodal physical and behavioral measures, specifically gait characteristics, body mass composition, and sleep parameters, could serve as digital biomarkers for estimating MCI severity. We recruited 80 patients diagnosed with MCI and classified them into early- and late-stage groups based on their Mini-Mental State Examination scores. Participants underwent clinical assessments, including the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet Korean Version, gait analysis using GAITRite, body composition evaluation via dual-energy X-ray absorptiometry, and polysomnography-based sleep assessment. Brain MRI was also performed to obtain structural imaging data. We evaluated the classification performance across various models, including support vector machines, random forest, multilayer perceptron, and convolutional neural network, using unimodal and multimodal datasets. Machine learning models trained on physical and behavioral data alone achieved a high classification accuracy (AUC up to 94%), comparable to that of MRI-based models, in differentiating early- and late-stage MCI. Combining physical and behavioral and MRI features yielded marginal improvements in the prediction performance. Gait velocity, lean body mass, and sleep efficiency were among the top predictors of cognitive function. Multimodal digital biomarkers or multimodal physical and behavioral signals can effectively estimate MCI severity and may offer a scalable, low-cost approach for early detection and monitoring of cognitive decline in real-world settings.