Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Shatnawi, Maad; Shatnawi, Anas; Alshara, Zakarea; Husari, Ghaith

doi:10.14569/ijacsa.2021.0120457

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…Different countries suffered a lot due to impreciseness and vagueness present in the symptoms of the virus based on the nature of the variant as per the geographical and environmental location, in this Fuzzy Expert System, is the better model for analysis and predicting the result. Maad Shatnawi et al (2021) [21] proposed a smart Fuzzy Inference System for the rare detection of COVID-19 based on the symptoms including fever, flu, dry cough, cold, breathing difficulties, throat sore and headache. Based on the Gaussian Membership Function designed a model with 13 linguistic Fuzzy Rules can assist the physician in identifying the diseases.…”

Section: Covid-19mentioning

confidence: 99%

Impact of fuzzy logic and its applications in medicine: A review

.¹,

Sharline²

2022

Int. J. Stat. Appl. Math.

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In recent years Artificial Intelligence Techniques (AITs) have now highly increased in solving complex and critical problems of more uncertainties that occur in the process of the system. AITs, offer the possibility of designing intelligent mathematical models involving Fuzzy Logic (FL), Artificial Neural Network (ANN), Genetic Algorithm (GA), and as such. In this, Fuzzy set theory is a highly suitable and applicable basis for developing knowledge-based systems in medical fields for diagnosing diseases and predicting risk factors. Fuzzy Logic decides uncertainty, incomplete, vagueness, and flexible structure with the use of intuitive methods. Due to the advent of digitalization in the field of the medical diagnostic process, Fuzzy Logic is highly suitable to make the decisions for diagnosis and treatment of different diseases. This article mainly focuses on a review of the applications of Fuzzy Logic in the Application areas of Medicines like Heart Disease, Asthma, Cold and Flu, Malaria, Parkinson's Disease, Diabetic Disease, Tuberculosis, Breast Cancer, and COVID-19 as well.

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Section: Covid-19mentioning

confidence: 99%

Impact of fuzzy logic and its applications in medicine: A review

.¹,

Sharline²

2022

Int. J. Stat. Appl. Math.

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“…The fuzzification process matches the crisp inputs to a fuzzy set membership degree using membership functions. (Shatnawi, Shatnawi, AlShara and Husari, 2021). These membership functions should incorporate the entire universe of discourse and represent a linguistic variable or a fuzzy set.…”

Section: Fuzzy Logic Approachmentioning

confidence: 99%

Estimation of Coal Seam Methane Contents Using Fuzzy Logic Method

Kursunoglu

2022

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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Kömürleştirme işlemi, kömürün hem fiziksel özelliklerinde hem de kimyasal yapısında değişikliklere neden olur. Sorpsiyon kapasitesi, kömürün karakteristik bir özelliğidir. Metan, yeraltı kömür madenlerinde önemli tehtidlerden biridir ve ayrıca kömür madenciliğinden kaynaklanan gaz emisyonlarının çevresel bir sorunudur. Metan içeriği, gaz göçü, birikimi ve üretimi gibi bir dizi sürece bağlıdır. Kalitesi ve miktarı, kömür damarının derecesine ve derinliğine bağlı olarak büyük ölçüde farklılık gösterir. Üretim derinliklerinin artmasıyla birlikte metan, patlama riski nedeniyle önemli bir tehlike haline gelmektedir. Bu nedenle metan içeriklerinin farklı ve değişen çalışma koşullarına göre tahmin edilmesi maden güvenliği açısından önemli bir konudur. Bu çalışmada kömür damarı metan içeriklerinin tahmininde hızlı ve güvenilir bir çözüm sunan Fuzzy Logic yöntemi tercih edilmiştir. Çalışmanın amacı, Bulanık Mantık yöntemi ile kömür damarı metan içeriklerini tahmin ederek olası maden kazalarını önlemek için alternatif bir yol önermektir. Model sonuçları yerinde metan içerikleri ile karşılaştırılmıştır. Sonuçlar, Bulanık Mantık modelinin %91 başarı oranı ile güvenilir bir tahmin aracı olabileceğini göstermektedir.

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“…In some healthcare settings, commercial test kits, swabs, PCR machines, or their expertise may be less available 13,15 . Additionally, CT scans have been found to have high rates of false negatives 14 . This is why combining diagnostic methods could improve COVID‐19 detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

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Background and AimsDue to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19.MethodsWe searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.ResultsA search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.ConclusionCDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis.

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Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Cited by 17 publications

References 33 publications

Impact of fuzzy logic and its applications in medicine: A review

Impact of fuzzy logic and its applications in medicine: A review

Estimation of Coal Seam Methane Contents Using Fuzzy Logic Method

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

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