Alhassoon Khaled, Alhsaon Mnahal Ali, Alsunaydih Fahad, Alsaleem Fahd, Salim Omar, Aly Saleh, Shaban Mahmoud
Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah, 52571, Saudi Arabia.
Department of Public Health , Qassim Health Cluster, 3032 At Tarafiyyah Rd, 6291, Buraydah, 52367, Saudi Arabia.
Heliyon. 2024 Jul 27;10(15):e35246. doi: 10.1016/j.heliyon.2024.e35246. eCollection 2024 Aug 15.
The worldwide health crisis triggered by the novel coronavirus (COVID-19) epidemic has resulted in an extensive variety of symptoms in people who have been infected, the most prevalent disorders of which are loss of smell and taste senses. In some patients, these disorders might occasionally last for several months and can strongly affect patients' quality of life. The COVID-19-related loss of taste and smell does not presently have a particular therapy. However, with the help of an early prediction of these disorders, healthcare providers can direct the patients to control these symptoms and prevent complications by following special procedures. The purpose of this research is to develop a machine learning (ML) model that can predict the occurrence and persistence of post-COVID-19-related loss of smell and taste abnormalities. In this study, we used our dataset to describe the symptoms, functioning, and disability of 413 verified COVID-19 patients. In order to prepare accurate classification models, we combined several ML algorithms, including logistic regression, k-nearest neighbors, support vector machine, random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The accuracy of the loss of taste model was 91.5 % with an area-under-cure (AUC) of 0.94, and the accuracy of the loss of smell model was 95 % with an AUC of 0.97. Our proposed modelling framework can be utilized by hospitals experts to assess these post-COVID-19 disorders in the early stages, which supports the development of treatment strategies.
新型冠状病毒（COVID-19）疫情引发的全球健康危机导致感染者出现了各种各样的症状，其中最常见的病症是嗅觉和味觉丧失。在一些患者中，这些病症偶尔可能会持续数月，并会严重影响患者的生活质量。目前，与COVID-19相关的味觉和嗅觉丧失尚无特定疗法。然而，通过对这些病症的早期预测，医疗服务提供者可以指导患者通过遵循特殊程序来控制这些症状并预防并发症。本研究的目的是开发一种机器学习（ML）模型，该模型可以预测COVID-19后相关嗅觉和味觉异常的发生和持续情况。在本研究中，我们使用数据集描述了413名确诊COVID-19患者的症状、功能和残疾情况。为了准备准确的分类模型，我们结合了多种ML算法，包括逻辑回归、k近邻、支持向量机、随机森林、极端梯度提升（XGBoost）和轻量级梯度提升机（LightGBM）。味觉丧失模型的准确率为91.5%，曲线下面积（AUC）为0.94，嗅觉丧失模型的准确率为95%，AUC为0.97。我们提出的建模框架可供医院专家在早期阶段评估这些COVID-19后的病症，这有助于制定治疗策略。
