Prediction of Severity of Covid-19-Infected Patients Using Machine Learning Techniques

Computers

Mohammad Shiblee¹  Aziz Alotaibi²  Adel Alshahrani³ 

[1] Deanship of University Development, Taif University, Taif 26571, Saudi Arabia;Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia;Rehabilitation Sciences Department, College of Applied Medical Sciences, Najran University, Najran 66446, Saudi Arabia;

关键词: severity prediction;    COVID-19;    machine learning;    feature selection;   

DOI  :  10.3390/computers10030031

来源: DOAJ

【 摘 要 】

Precisely assessing the severity of persons with Covid-19 at an early stage is an effective way to increase the survival rate of patients. Based on the initial screening, to identify and triage the people at highest risk of complications that can result in mortality risk in patients is a challenging problem, especially in developing nations around the world. This problem is further aggravated due to the shortage of specialists. Using machine learning (ML) techniques to predict the severity of persons with Covid-19 in the initial screening process can be an effective method which would enable patients to be sorted and treated and accordingly receive appropriate clinical management with optimum use of medical facilities. In this study, we applied and evaluated the effectiveness of three types of Artificial Neural Network (ANN), Support Vector Machine and Random forest regression using a variety of learning methods, for early prediction of severity using patient history and laboratory findings. The performance of different machine learning techniques to predict severity with clinical features shows that it can be successfully applied to precisely and quickly assess the severity of the patient and the risk of death by using patient history and laboratory findings that can be an effective method for patients to be triaged and treated accordingly.

【 授权许可】

Unknown