The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.

最近由新型冠状病毒SARS-Cov2引起的呼吸道疾病COVID-19暴发是一个严重而紧迫的全球性问题。在缺乏有效治疗的情况下，主要的遏制策略是通过隔离受感染的个体来减少传染。然而，隔离未受影响的个体是非常不可取的。为帮助快速确定治疗和隔离需求，确定可疑感染病例呈现的特征是阳性诊断的最佳预测指标将很有用。这可以通过分析患者特征，病例轨迹，合并症，症状，诊断和结果来完成。我们开发了一个模型，该模型采用了受监督的机器学习算法来识别表示特征，以高度准确地预测COVID-19疾病的诊断。检查的特征包括有关个体的详细信息，例如年龄，性别，发烧观察，旅行史以及临床详细信息，例如咳嗽的严重程度和肺部感染的发生率。我们对收集到的数据实施了多种机器学习算法并将其应用到其中，并发现XGBoost算法以最高的准确度（> 85％）来预测和选择可正确指示所有年龄组COVID-19状态的特征。统计分析表明，最常见和最显着的预测症状是发烧（41.1％），咳嗽（30.3％），肺部感染（13.1％）和流鼻涕（8.43％）。虽然接受检查的54.4％的人没有出现任何可用于诊断的症状，但我们的工作表明，对于其余患者，我们的预测模型可以显着改善对COVID-19状况的预测，