NHANES-based machine learning for cognitive impairment classification and blood and hearing threshold characterization in age-related hearing loss

Abstract

Objective

This study aims to develop a machine learning-based classification model for cognitive impairment (CI) in elderly deaf patients and analyze the contributions of blood indices and hearing characteristics in identifying CI.

Methods

Blood and audiometric data from 833 elderly deaf patients across three NHANES cycles were used to build a classification model with five algorithms: Logistic Regression, Random Forest (RF), XGBoost, Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The optimal model was selected to rank feature importance.

Results

The RF model, with an AUC of 0.834, performed best. Key predictors of CI included gender, systolic blood pressure, PTA+3kHz, neutrophil percentage, calcium, 6kHz hearing threshold, glycated hemoglobin, lymphocyte count,etc.

Conclusion

Hematological markers and hearing thresholds, especially the 3kHz threshold, are significant in identifying CI in ARHL, suggesting the need for further clinical exploration.