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Updated: Aug 5, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Prediction Model for Mild Cognitive Impairment in Older Chinese Patients With Cerebral Small Vessel Disease Based on
Peng Gao1, Jingjing Su2, Xiaoming Ma3,4,5
1Department of Clinical Psychology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Objective:
This study aims to evaluate cognitive function in patients with Cerebral Small Vessel Disease (CSVD) and investigate its association with variables such as serum Insulin-like Growth Factor-1 (IGF-1). Artificial intelligence algorithms, specifically eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), were utilized for analysis and interpretation.
Methods:
A total of 216 patients diagnosed with CSVD were enrolled from the Department of Neurology, Third Affiliated Hospital of Soochow University, between November 2019 and August 2020. Clinical and biochemical data-including triglycerides, total cholesterol, low-density lipoprotein cholesterol, fasting blood glucose, glycosylated hemoglobin (HbA1c), fasting insulin, C-peptide, anti-human insulin antibodies, and IGF-1-were obtained under standardized laboratory protocols. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Based on cognitive performance, patients were categorized into CSVD with cognitive impairment and CSVD without cognitive impairment.
Results:
The original cohort included 216 patients with CSVD, comprising 50 patients with MCI and 166 patients without MCI. To address class imbalance during model development, SMOTE-NC was applied within the development dataset. The XGBoost model achieved a precision of 0.790, recall of 0.901, F1 score of 0.820, accuracy of 0.833, and Cohen's kappa coefficient of 0.667 in the validation cohort. Feature importance analysis identified key predictors, while SHAP values enabled intuitive visualization of each feature's impact. Decision Curve Analysis (DCA) confirmed the model's clinical utility and net benefit, underscoring its potential for early MCI detection and targeted intervention to improve patient outcomes.
Conclusion:
Combining XGBoost and SHAP enhances model interpretability, facilitating the identification of critical risk factors such as reduced IGF-1 levels in MoCA-defined MCI in patients with CSVD. This AI-driven approach offers a valuable tool for informing treatment decisions and optimizing healthcare resource allocation.
