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

IF 2.5 3区医学 Q3 GERIATRICS & GERONTOLOGY

Geriatric Nursing Pub Date : 2025-03-13 DOI:10.1016/j.gerinurse.2025.02.011

Zhanhang Zheng (Bachelor,Master's Candidate) , Shuimei Li (Bachelor) , Ruilin Li (Doctor) , Shuhong Qin (Bachelor,Master's Candidate) , Wenjuan Wang (Bachelor,Master's Candidate) , Chenxingzi Wu (Bachelor,Master's Candidate)

{"title":"NHANES-based machine learning for cognitive impairment classification and blood and hearing threshold characterization in age-related hearing loss","authors":"Zhanhang Zheng (Bachelor,Master's Candidate) , Shuimei Li (Bachelor) , Ruilin Li (Doctor) , Shuhong Qin (Bachelor,Master's Candidate) , Wenjuan Wang (Bachelor,Master's Candidate) , Chenxingzi Wu (Bachelor,Master's Candidate)","doi":"10.1016/j.gerinurse.2025.02.011","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>Hematological markers and hearing thresholds, especially the 3kHz threshold, are significant in identifying CI in ARHL, suggesting the need for further clinical exploration.</div></div>","PeriodicalId":56258,"journal":{"name":"Geriatric Nursing","volume":"63 ","pages":"Pages 8-14"},"PeriodicalIF":2.5000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geriatric Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0197457225000722","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Geriatric Nursing 医学-护理

CiteScore

3.80

自引率

7.40%

发文量

257

审稿时长

>12 weeks

期刊介绍： Geriatric Nursing is a comprehensive source for clinical information and management advice relating to the care of older adults. The journal''s peer-reviewed articles report the latest developments in the management of acute and chronic disorders and provide practical advice on care of older adults across the long term continuum. Geriatric Nursing addresses current issues related to drugs, advance directives, staff development and management, legal issues, client and caregiver education, infection control, and other topics. The journal is written specifically for nurses and nurse practitioners who work with older adults in any care setting.