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网络应用程序使用机器学习来预测矿工心血管疾病和高血压

Sohrab Effati^1,2, Alireza Kamarzardi-Torghabe³, Fatemeh Azizi-Froutaghe⁴

¹Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran. s-effati@um.ac.ir.

Scientific reports

|December 31, 2024

概括

机器学习准确地预测了矿工的心血管疾病和高血压. 一个网络应用程序为职业健康管理提供个性化的风险评估和干预措施.

科学领域:

职业健康职业健康职业健康
机器学习应用机器学习应用
心血管健康心血管健康

背景情况:

由于职业和环境危害,矿工面临心血管疾病 (CVD) 和高血压 (HTN) 的高风险.
早期检测和干预对于降低高风险人群的发病率和死亡率至关重要.

研究的目的:

开发和验证基于机器学习的Web应用程序,用于预测矿工中CVD和HTN风险.
为预防性健康策略提供个性化的风险评估和可操作的见解.

主要方法:

利用了699名矿工 (2016-2020年) 的数据集,包括人口统计,职业,生活方式和医疗数据.
进行了数据预处理和特征工程,随后使用诸如随机森林,物流回归和支持矢量机器等算法进行模型训练.
随机森林算法被选为表现最好的模型.

主要成果:

随机森林模型实现了高预测准确度:HTN为99%,CVD为97%.
该模型确定了关键的风险因素,包括年龄,就业史,家庭健康,以及对尘埃和噪音的暴露.
开发的Web应用程序提供个性化的风险评分和干预建议.

结论:

机器学习,特别是随机森林算法,提供了一个非常准确的工具,用于在职业环境中预测CVD和HTN.
该网络应用程序通过早期风险识别和有针对性的干预措施,促进了积极的职业健康管理.
这种方法证明了人工智能的潜力,可以增强暴露于重大环境和职业风险的工人的预防性医疗保健.

关键词:

心血管疾病是什么心血管疾病护理申请护理申请在高血压的高血压.矿山工人矿山工人矿山工人这是一个Web应用程序.

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