Zhou Mengran, Bian Kai, Hu Feng, Lai Wenhao
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China.
Front Bioeng Biotechnol. 2022 Jul 11;10:935481. doi: 10.3389/fbioe.2022.935481. eCollection 2022.
Coal miners' occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners' working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.
煤矿工人的职业健康是煤矿安全生产的关键部分。准确识别异常体征是预防职业病和改善矿工工作环境的关键。人工评估矿工身体健康状况时存在诸多问题，如体征参数过多、诊断效率低、漏诊和误诊等。为解决这些问题，采用机器学习算法来识别有异常体征的矿工。我们提出了一种融合弹性网（EN）和最大相关最小冗余（mRMR）的特征筛选策略，以建立识别异常体征的模型并获取关键体征。首先，通过特征构建技术将原始的21项体征扩展到25项。然后，使用EN删除冗余体征。最后，将mRMR与基于引力搜索算法的智能优化算法支持向量分类（GSA - SVC）相结合，进一步简化其余12项相对重要的体征，并用6项体征的数据获得最优模型。此时，测试集的准确率、精确率、召回率、特异性、G均值和马修斯相关系数分别为97.50%、97.78%、97.78%、97.14%、0.98和0.95。实验结果表明，所提出的策略以最小的特征数提高了模型性能，实现了对异常煤矿工人的准确识别。该结论可为职业健康早期的智能分类与评估提供参考依据。
