使用機器學習演算法XGBoost探討中部州農業生產者佩戴聽力保護裝置的相關因素

// Using the Machine Learning Algorithm XGBoost to Understand the Correlates of Wearing Hearing Protection in Central States' Agricultural Producers

本研究分析美中部七州農業工人聽力保護使用情況｜採用調查數據與XGBoost機器學習方法，發現佩戴呼吸與化學個人防護裝備（PPE）及年齡為最強預測因子｜噪音暴露影響較小，年輕作業者多使用各類PPE｜結果顯示聽力保護依賴個人風險感知，建議防護推廣應強調安全動機提升。

// This study examined factors influencing hearing protection use among agricultural workers in seven Central US states using survey data and XGBoost analysis. The main finding was that wearing respiratory and chemical PPE, along with age, were the strongest predictors of hearing protection use, while noise exposure was less important. Younger operators tended to wear PPE more frequently across all types. The results suggest hearing protection use is more linked to individual risk perception than actual noise exposure, highlighting the need for prevention programs to focus on safety motivation.

Published 2026年6月5日