Development and verification of a machine learning-driven predictive model for cognitive impairment in Age-related Hearing Loss

Abstract

Objective: The objective is to utilize machine learning algorithms to create a predictive model for cognitive impairment in age-relate hearing loss. Methods:For this study, we gathered demographic information, conducted audiometric examinations, assessed cognitive abilities, and performed blood biochemical tests using data from NHANES. We then identified patients who fit the criteria based on the NHANES criteria. The LASSO regression method was employed to identify the determinants of cognitive impairment in age-related hearing loss. Additionally, five different machine learning algorithms were utilized to develop a predictive model for cognitive impairment in this population. Data from clinical trials were gathered between January 2024 and May 2024 to externally validate the model's dependability. Results:The study comprised a total of 521 elderly adults with hearing loss, out of which 140 (26.8%) had cognitive impairment. The LASSO regression method was used to filter five factors: education level, alkaline phosphatase, globulin, creatinine, and eosinophil percentage. All of these predictors were then included in the machine learning approach for training the model.The XGB model exhibited consistent performance in both the training set (AUC=0.881) and the test set (AUC=0.868), while also demonstrating a moderate level of discrimination (AUC=0.706). Conclusion:This study successfully developed a predictive model for cognitive impairment in age-related hearing loss using machine learning. The model demonstrates a satisfactory level of reliability and validity across several datasets. The utilization of machine learning models can serve as a potent instrument for healthcare providers in detecting cognitive impairment in the senior hearing loss population at an early stage.