Cognitive Impairment Prediction

Cognitive Impairment Prediction predicts the risk of cognitive impairment in elderly hypertensive patients in the United States using a logistic regression machine learning model trained on National Health and Nutrition Examination Survey (NHANES) data.

Key Features:

• Multivariable Risk Prediction: The final model incorporates 12 predictors selected through the modeling process.
• Multiple Algorithm Comparison: Seven machine learning algorithms—logistic regression (LR), extreme gradient boosting (XGB), decision tree (DT), categorical boosting (CatBoost), random forest (RF), light gradient boosting machine (LGBM), and support vector machine (SVM)—were trained and compared to identify the optimal model.
• Model Performance Evaluation: Performance was assessed using area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
• Interpretability Analysis: The SHapley Additive exPlanation (SHAP) method was applied for feature importance interpretation and identification of key variables contributing to predictions.

Scientific Applications:

• Cognitive Impairment Risk Stratification: Enables clinicians to quickly identify high-risk elderly hypertensive patients for targeted prevention and intervention strategies.
• Risk Factor Identification: Identifies key associated risk factors for cognitive impairment through machine learning-based analysis of population health survey data.

Methodology:

Data were obtained from 19,931 participants enrolled in the 2011–2012 and 2013–2014 cycles of the National Health and Nutrition Examination Survey (NHANES). The dataset was randomly split into training and test sets (70:30). Seven machine learning algorithms were trained to predict cognitive impairment. The SHapley Additive exPlanation (SHAP) method was applied for feature importance interpretation. The final model was logistic regression, achieving an AUC of 0.791 on the test set.