A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population

作者：Yi Wang, Xinmeng Yao, Dahui Wang, Chengyin Ye, Liangwen Xu · 发表于：BMC Public Health · 年份：2024 · DOI：10.1186/s12889-024-18636-1 · 被引用次数：10 · 研究领域：Hearing Loss and Rehabilitation、Hearing, Cochlea, Tinnitus, Genetics、Vestibular and auditory disorders

BACKGROUND: Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in community settings. Therefore, the purpose of this study was to develop a cost-effective HI screening model for the general population using machine learning (ML) methods and data gathered from community-based scenarios, aiming to help improve the hearing-related health outcomes of community residents. METHODS: This study recruited 3371 community residents from 7 health centres in Zhejiang, China. Sixty-eight indicators derived from questionnaire surveys and routine haematological tests were delivered and used for modelling. Seven commonly used ML models (the naive Bayes (NB), K-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), boosting, and least absolute shrinkage and selection operator (LASSO regression)) were adopted and compared to develop the final high-frequency hearing impairment (HFHI) screening model for community residents. The model was constructed with a nomogram to obtain the risk score of the probability of individuals suffering from HFHI. According to the risk score, the population was divided into three risk stratifications (low, medium and high) and the risk factor characteristics of each dimension under different risk stratifications were identified. RESULTS...