Nannan Sun, Ya Yang, Lingling Tang, Zhen Li, Yining Dai, Wan Xu, Xiaoliang Qian, Hainv Gao and Bin Ju*
Objective: To improve the timeliness for the early COVID-19 infection diagnosis, it is essential to develop a decision- making tool to assist early diagnosis of COVID-19 patients in fever clinics.
Materials and methods: This paper aims at extracting risk factors from clinical data of 912 early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and a deep learning-based method for diagnosis of early COVID-19.
Results: The results show that the LR predictive model presents a higher specificity rate of 0.95, an Area Under the receiver operating Curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyse the contribution of the factors to the predictive models.
Discussions: Under the background of COVID-19 pandemic, the early diagnosis of COVID-19 still face severe challenges, a decision-making tool assisting early diagnosis of COVID-19 patients is vital for fever clinics.
Conclusions: Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.
Published Date: 2021-04-22; Received Date: 2021-04-01