Prediction System for Diagnosis and Detection of Coronavirus Disease-2019 (COVID-19): A Fuzzy-Soft Expert System

In early December 2019, a new virus named 2019 novel coronavirus (2019-nCoV) appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 (i.e., shortness of breath, sore throat, cough, fever, and age). We will use the algorithm proposed by Kong et al. to detect these patients who may suffer from COVID-19. In this way, the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not. Finally, we present the comparison between the present system and the fuzzy expert system.

Automated summary

In early December 2019, a new virus called 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China, leading to the global COVID-19 pandemic. This study proposes a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The system consists of five portions and is based on an exploratory study of sixty patients with symptoms similar to COVID-19. It utilizes an algorithm to detect potential COVID-19 patients and can assist physicians in making diagnoses.