Prediction of COVID-19 based on neurological manifestations using a fuzzy logic system

Abstract

Abstract Background COVID-19, caused by the SARS-CoV-2 virus, is one of the most known pandemics ever affecting human life and global economics. Recently, it has shown several symptoms related to different organ systems, including the nervous system, represented in some reported neurological manifestations. Therefore, a smart prediction system that can determine the likelihood and certainty of having COVID-19 based on those neurological manifestations can help in early detection of the disease, which helps in diagnosis and limiting the prevalence of COVID-19. Patients and methods This study involved a comprehensive data collection process. We gathered information from thousands of patients, encompassing both neurological and non-neurological manifestations of COVID-19. This data, derived from various research works, including mild and moderate cases, was then subjected to rigorous statistical analysis. The results of this analysis formed the basis for the design of a fuzzy interference system (FIS), which utilizes a fuzzy logic approach to determine the certainty of COVID-19 based on neurological symptoms. Results Statistical analysis of the collected data showed neurological symptoms in all surveyed cases in the first week of the COVID-19 presentation. Headache has been reported in 70–80% of all cases; anosmia–dysgeusia showed up in 50–60% of total cases; Myalgia presented in 40–45% of all cases; Fatigue was there in 30–35% of the surveyed cases; dizziness was recorded in 30–35% of patients; 0–10% of subjects showed noncommon symptoms like numbness, migraine, loss of concentration, and seizures. By applying these statistical results to the fuzzification process and developing the rulesets, the fuzzy logic-based forecasting system could determine the certainty of COVID-19 with high accuracy, reaching 95% by comparing it with the clinical data. Conclusions Surveying neurological and non-neurological symptoms of thousands of COVID-19 patients in many related literature showed neurological manifestations in all patients with different ratios and weights, including mild and moderate cases, by statistically analyzing these data to form the rulesets of a predesigned fuzzy logic-based forecasting system. The fuzzy logic system was able to yield a successful prediction of the likelihood of having COVID-19 in a group of patients based on their neurological symptoms with an accuracy of 95% by comparing the predicted data with the clinical data.