Objective: Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. Results: Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC. The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. Our result shows that all proposed models achieved satisfactory performance, with the AUC values ranging from 0.826 to 0.850. Among the seven predictive models, the gradient boosting tree model outperformed other models, achieving an AUC of 0.850. Conclusions: Our risk prediction model shows great promise in automating real-time diagnosis, supporting healthcare practitioners to target high-risk individuals efficiently, and helping healthcare practitioners tailor proactive strategies to prevent the onset or delay the progression of the chronic diseases.

中文翻译：

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使用机器学习模型预测美国工作人口中多种慢性病的风险

目的：慢性病已成为医疗保健行业中最普遍和最昂贵的健康状况，恶化生活质量，对工作效率产生不利影响，并花费惊人的医疗资源。然而，很少有研究基于工作人群对多种慢性病（MCC）进行预测分析。结果：七种机器学习算法用于支持医疗保健从业者对 MCC 风险的决策。这些模型是使用医疗保健提供者收集的 451,425 名工作人口的检查数据开发和验证的。我们的结果表明，所有提出的模型都取得了令人满意的性能，AUC 值在 0.826 到 0.850 之间。在七个预测模型中，梯度提升树模型的表现优于其他模型，AUC 为 0.850。结论：我们的风险预测模型在自动化实时诊断、支持医疗保健从业者有效地针对高风险个体以及帮助医疗保健从业者定制主动策略以预防慢性病的发作或延缓其进展方面显示出巨大的前景。