Fuzzy Based Integrated Diagnostic System for Neurodevelopmental Disorders

Kaur, Gurjot; Kakkar, Deepti

doi:10.1109/spin.2019.8711674

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Siddiqui et al developed an adaptive hierarchical Mamdani fuzzy expert system for the detection of arthritis. In their research, [7] Hepatitis Indirect approach for fuzzy system modeling Guzmán et al ( 2017) [8] Blood pressure Neuro-fuzzy hybrid model Meza-Palacios et al (2017) [9] Type-2 diabetes mellitus Fuzzy expert system based on experts' guidelines Sadat Asl and Zarandi (2017) [10] Leukemia Mamdani-style Type-2 fuzzy expert system Zarandi et al (2017) [11] Heart disease Expert system based on fuzzy bayesian network Motlagh et al (2018) [12] Depressive disorder Web-based fuzzy expert system Caliwag et al (2018) [13] Venereal diseases Mobile expert system based on fuzzy logic Muhammad et al ( 2018) [14] Coronary artery disease Expert system with developed knowledge acquisition Nazari et al ( 2018) [15] Heart diseases Fuzzy inference-fuzzy analytic hierarchy process-based Soltani et al ( 2018) [16] Glaucoma Expert fuzzy logic & image processing methods Tuan et al ( 2018) [17] Dental disorders Fuzzy computing & image processing methods Terrada et al ( 2018) [18] Cardiovascular diseases Fuzzy medical system using risk factors based on data Ahmad et al ( 2019) [19] Hepatitis B Multilayer Mamdani fuzzy inference system Raza et al ( 2019) [20] Erythemato squamous Expert system based on fuzzy rules Arji et al ( 2019) [21] Infectious disease Fuzzy inference & rule-based fuzzy logic, ANFIS Kaur and Kakkar (2019) [22] Neurodevelopmental disorders Fuzzy based-systems based on co-morbid factors Sadat Asl (2019) [23] Leukemia Two-stage expert system based on type-2 fuzzy logic Mirmozaffari (2019) [24] Liver diseases Expert system based on the VP-Expert shell Mujawar and Jadhav (2019) [25] Diabetes Web-based fuzzy expert system Mutawa and Alzuwawi (2019) [26] Uveitis Multilayered rule-based expert system Sajadi et al ( 2019) [27] Hypothyroid...…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

Asl

Ershadi

Sotudian

et al. 2022

IDT

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The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with Covid-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with this issue, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat in ICU. In this study, an interval type-2 fuzzy expert system is proposed for prediction of ICU admission in COVID-19 patients. For this prediction task, we also developed an adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these fuzzy systems are compared to some well-known classification methods such as Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other system modeling techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

Asl

Ershadi

Sotudian

et al. 2022

IDT

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“…According to John McCarthy, who is the founder of the term "artificial intelligence", AI is the science that involves the creation of intelligent machines. A remarkable definition is "the study of ideas that allow the machines to be smart" [9]. Artificial intelligence is not limited in executing simple orders but is able to automatically execute complicated tasks that take a lot of time and effort for humans [10].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Autism Assessment

Anagnostopoulou

Alexandropoulou

Lorentzou

et al. 2020

Int. J. Emerg. Technol. Learn.

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The current paper review gives a brief and representative description of the role that artificial intelligence plays nowadays at the assessment of autism. Therefore, many researchers note that artificial intelligence plays a notable role in the early diagnosis of autism since it helps the clinicians shorten the diagnose process and have more accurate results. Thus, the research team of this paper presents some applications of artificial intelligence that are used already or are in a preliminary phase aiming to highlight the use of smart technology in the diagnosing process of autism. Lastly, it is worth noting that an early and accurate diagnose is the key point for an individualized and successful intervention which aids the academic as well as the personal de-velopment of the child.

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