The creation and verification of a detection model for mild cognitive impairment by employing eye-tracking and gait metrics

BackgroundMild cognitive impairment is a prodromal stage of dementia, and early identification is crucial for prognosis.ObjectiveThis study aims to create and validate a machine learning model for diagnosing mild cognitive impairment (MCI) using eye movement and gait analysis data.MethodsTo facilitate model training and internal validation, a cohort of 235 patients was recruited from the Memory Clinic at Xi'an NO.3 Hospital between August 2024 and November 2025. In addition, data from 71 patients were randomly selected to form an independent test set. Feature selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression. Subsequently, various machine learning classifiers were compared. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC) and decision curve analysis. To evaluate model interpretability, SHapley Additive exPlanations (SHAP) were employed.ResultsThe study involved 235 participants, divided into mild cognitive impairment (MCI) (n = 130) and healthy control (HC) (n = 105) groups. The final prediction model used four features: gait speed during a dual-task test, ground reaction force in a single-task test, antisaccade task accuracy, and noise rate in a saccade-to-pursuit task. The Gaussian Naive Bayes (GNB) classifier showed excellent performance with an AUC of 0.952 (95% CI: 0.923-0.981) in the validation group and 0.944 (95% CI: 0.912-0.967) in the test set.ConclusionsThe GNB model, combining eye movement and gait parameters, enables early MCI detection with high accuracy and practical clinical use.