Fuzzy Logic Inference System for Identification and Prevention of Coronavirus (COVID-19)

Sharma, Manoj; Dhiman, Nitesh

doi:10.35940/ijitee.f4642.049620

Cited by 18 publications

(12 citation statements)

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“…Dhiman and Sharma [5] proposed a fuzzy inference system to diagnose COVID-19 disease using six input factors: ethanol, atmospheric temperature (AT), body temperature (BT), breath shortness (BS), cough, and cold, with the output factor divided into three linguistic categories denoting the severity level of the infected patients. They discovered that if the atmospheric temperature is moderate, there is a higher consumption of ethanol, and a somewhat higher body temperature, the infected patient would have a normal severity level.…”

Section: Review Of Related Literaturesmentioning

confidence: 99%

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

Ahmed¹,

Rahman²,

Farooqui³

et al. 2021

MMEP

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The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%.

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Section: Review Of Related Literaturesmentioning

confidence: 99%

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

Ahmed¹,

Rahman²,

Farooqui³

et al. 2021

MMEP

View full text Add to dashboard Cite

show abstract

“…However, they are less accurate compared to wrapper functions but faster to compute. Since accuracy and reliability are essential in medical diagnostic systems, the employed feature selection module in the pre-processing phase relies on the wrapper functions to calculate the weights [46]. The relatively long computation time in the wrapper function for calculating the weights has no impact on the model's efficiency, as it occurs only once.…”

Section: Pre-processing Phasementioning

confidence: 99%

Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients

et al. 2021

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Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%.

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“…Other studies adopting type-1 fuzzy concept for COVID-19 pandemic prediction include [17] - [21]. Unlike the type-1 fuzzy sets (T1FSs) with precise MF, type-2 fuzzy sets (T2FSs) [22] have MFs that are themselves type-1 FS (lower and upper T1FS).…”

Section: Related Workmentioning

confidence: 99%

Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System

2021

IJATCSE

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COVID-19 is a virus known to emanate from Wuhan, China in December 2019. COVID-19 spread widely to nearby countries like Japan and Korea, followed by Europe and America and later to Africa. Particularly, South Africa and Egypt have been worst hit by the virus. Generally, the COVID-19 data is highly uncertain and requires fuzzy logic approaches for the effective handling of these uncertainties. This study therefore presents the prediction of COVID-19 cases in South Africa and Egypt using interval type-2 fuzzy logic system with Takagi-Sugeno-Kang fuzzy inference and neural network learning. The parameters of the model are adapted using gradient descent backpropagation approach. The proposed model is found to outperform type-1 fuzzy logic system and artificial neural network in terms of the root mean squared error, mean absolute percentage error and mean absolute error

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Fuzzy Logic Inference System for Identification and Prevention of Coronavirus (COVID-19)

Cited by 18 publications

References 0 publications

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients

Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System

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