基于行为数据的慢性病诊断
Chronic Disease Diagnoses Using Behavioral Data
AI总结：
本文利用629名参与者3个月的行为数据训练机器学习模型，在不依赖临床医疗数据的情况下实现糖尿病、高脂血症和高血压的早期诊断，准确率分别达80.2%、71.3%和81.2%，并通过Shapley分析识别出与文献一致的关键影响特征。
摘要：
慢性病的早期检测对医疗保健有益，因为它为及时干预提供了黄金机会。尽管已有大量先前研究成功使用机器学习（ML）模型进行疾病诊断，但它们高度依赖医疗数据，而大多数慢性病早期患者的医疗数据十分稀缺。在本文中，我们旨在利用自行收集的行为数据来诊断高血糖（糖尿病）、高脂血症和高血压（统称为3H），从而在不使用临床环境中收集的医疗数据的情况下实现3H的早期检测。具体而言，我们在为期3个月的研究期间收集了629名参与者的日常行为数据，并在数据预处理后训练了多种ML模型。实验结果表明，仅使用参与者上传的行为数据，我们就能实现准确的3H诊断：糖尿病、高脂血症和高血压的诊断准确率分别为80.2%、71.3%和81.2%。此外，我们对训练后的模型进行了Shapley分析，以识别每种疾病最具影响力的特征。所识别出的有影响力特征与文献中报告的结果一致。
英文摘要：
Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they highly rely on medical data, which are scarce for most patients in the early stage of the chronic diseases. In this paper, we aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data, thus, enable the early detection of 3H without using medical data collected in clinical settings. Specifically, we collected daily behavioral data from 629 participants over a 3-month study period, and trained various ML models after data preprocessing. Experimental results show that only using the participants' uploaded behavioral data, we can achieve accurate 3H diagnoses: 80.2\%, 71.3\%, and 81.2\% for diabetes, hyperlipidemia, and hypertension, respectively. Furthermore, we conduct Shapley analysis on the trained models to identify the most influential features for each type of diseases. The identified influential features are consistent with those reported in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
