Research on Threshold Optimization and Variability-Based Digital Biomarker Approaches Through MMSE-Lifelog Multimodal...

Background: Early screening of cognitive impairment is essential for timely clinical intervention; however, conventional cognitive tests such as the Mini-Mental State Examination (MMSE) rely on fixed thresholds that may not be optimal in real-world screening settings. Methods: This study developed a threshold-aware multimodal screening framework integrating MMSE item-level scores with wearable-derived sleep and physical activity lifelog data. A dataset of 174 adults was analyzed and categorized into cognitively normal (CN), mild cognitive impairment (MCI), and dementia, with MCI and dementia combined as an impaired group. A CatBoost-based binary classification model was trained using five-fold cross-validation. The optimal decision threshold was determined by maximizing balanced accuracy using out-of-fold predictions. Results: The optimized threshold (0.49) achieved an accuracy of 0.818 and a balanced accuracy of 0.728 on the validation set. The recall values were 0.885 for CN and 0.571 for the impaired group, with an area under the ROC curve of 0.676. Feature importance and stability analyses showed that variability-related sleep and activity features were consistently selected across folds. Conclusions: These findings suggest that threshold optimization combined with multimodal lifelog integration may improve the interpretability of screening decisions. Variability-based lifelog features may provide complementary information alongside MMSE, although their role remains exploratory and requires further validation in larger and longitudinal cohorts.