PDF(Pubmed)
Abstract：
Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person\'s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.
摘要：
背景：人工智能的空前发展彻底改变了医疗保健行业。在下一代医疗保健系统中，自我诊断将是个性化医疗服务的关键。在COVID-19大流行期间，移动医疗等新的筛查和诊断方法在减少疾病传播和克服地理障碍方面处于有利地位。本文提出了一种非侵入性筛查方法，可以使用机器学习技术从视觉上可观察的特征中预测人的健康状况。使用相机或移动设备获取患者的面部和皮肤表面等图像，并对其进行分析，以得出临床推理和对患者健康的预测。方法：具体而言，提出了一种两级分类方法。所提出的分层模型通过在层次结构的节点处训练二元分类器来选择类。然后使用一组特定于类别的简化特征集进行预测。结果：一级和二级分类的测试准确率分别为86.87％和76.84％。实证结果表明，所提出的方法产生了良好的预测结果，同时大大减少了计算时间。结论：研究表明，使用具有成本效益的机器学习方法，可以根据一个人的面部外观来预测他/她的健康状况。
