A Proposed Fuzzy Model for Diseases Diagnosis

Alkholy, Eman Nabil; Aboutabl, Amal Elsayed; Haggag, Mohamed H.

doi:10.35940/ijeat.c6419.029320

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Fuzzy logic is a popular methodology for dynamic systems control, particularly when the inputs are not easily classified into binary values. [12][13][14][15][16] It classifies inputs into a linguistic or descriptive term using a set of "membership functions", which maps inputs to values between 0 and 1, enabling weighted classification into the linguistic sets. Inputs can then be evaluated by a set of Boolean rules that provide the desired output.…”

Section: Dual-input Fuzzy-logic Controller Designmentioning

confidence: 99%

Adaptive closed‐loop resuscitation controllers for hemorrhagic shock resuscitation

et al. 2023

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Background After hemorrhage control, fluid resuscitation is the most important intervention for hemorrhage. Even skilled providers can find resuscitation challenging to manage, especially when multiple patients require care. In the future, attention‐demanding medical tasks like fluid resuscitation for hemorrhage patients may be reassigned to autonomous medical systems when availability of skilled human providers is limited, such as in austere military settings and mass casualty incidents. Central to this endeavor is the development and optimization of control architectures for physiological closed‐loop control systems (PCLCs). PCLCs can take many forms, from simple table look‐up methods to widely used proportional–integral–derivative or fuzzy‐logic control theory. Here, we describe the design and optimization of multiple adaptive resuscitation controllers (ARCs) that we have purpose‐built for the resuscitation of hemorrhaging patients. Study Design and Methods Three ARC designs were evaluated that measured pressure–volume responsiveness using different methodologies during resuscitation from which adapted infusion rates were calculated. These controllers were adaptive in that they estimated required infusion flow rates based on measured volume responsiveness. A previously developed hardware‐in‐loop test platform was used to evaluate the ARCs implementations across several hemorrhage scenarios. Results After optimization, we found that our purpose‐built controllers outperformed traditional control system architecture as embodied in our previously developed dual‐input fuzzy‐logic controller. Discussion Future efforts will focus on engineering our purpose‐built control systems to be robust to noise in the physiological signal coming to the controller from the patient as well as testing controller performance across a range of test scenarios and in vivo.

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Section: Dual-input Fuzzy-logic Controller Designmentioning

confidence: 99%

Adaptive closed‐loop resuscitation controllers for hemorrhagic shock resuscitation

et al. 2023

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An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic

Şimşek

Yangın

2022

Health Technol.

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COVID-19 disease is an outbreak that seriously affected the whole world, occurred in December 2019, and thus was declared a global epidemic by WHO (World Health Organization). To reduce the impact of the epidemic on humans, it is important to detect the symptoms of the disease in a timely and accurate manner. Recently, several new variants of COVID-19 have been identified in the United Kingdom (UK), South Africa, Brazil and India, and preliminary findings have been suggested that these mutations increase the transmissibility of the virus. Therefore, the aim of this study is to construct a support system based on fuzzy logic for experts to help detect of COVID-19 infection risk in a timely and accurate manner and to get a numerical output on symptoms of the virus from every person. The decision support system consists of three different sub and one main Mamdani type fuzzy inference systems (FIS). Subsystems are Common- Serious symptoms (First), Rare Symptoms (Second) and Personal Information (Third). The first FIS has five inputs, fever-time, cough-time, fatigue-time, shortness of breath and chest pain/dysfunction; the second FIS has four inputs, Loss of Taste/Smell, Body Aches, Conjuctivitis, and Nausea/Vomiting/Diarrhea; and the third FIS has three inputs, Age, Smoke, and Comorbidities. Then, we obtain personal risk index of individual by combining the outputs of these subsystems in a final FIS. The results can be used by health professionals and epidemiologists to make inferences about public health. Numerical output can also be useful for self-control of an individual.

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Dual Input Fuzzy Logic Controllers for Closed Loop Hemorrhagic Shock Resuscitation

et al. 2022

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Hemorrhage remains a leading cause of preventable death in emergency situations, including combat casualty care. This is partially due to the high cognitive burden that constantly adjusting fluid resuscitation rates can require, especially in austere or mass casualty situations. Closed-loop control systems have the potential to simplify hemorrhagic shock resuscitation if properly tuned for the application. We have previously compared 4 different controller types using a hardware-in-loop test platform that simulates hemorrhagic shock conditions, and we found that a dual input—(1) error from target and (2) rate of error change—fuzzy logic (DFL) controller performed best. Here, we highlight a range of DFL designs to showcase the tunability the controller can have for different hemorrhage scenarios. Five different controller setups were configured with different membership function logic to create more and less aggressive controller designs. Overall, the results for the different controller designs ranged from reaching the setup rapidly but often overshooting the target to more conservatively approaching the target, resulting in not reaching the target during high active hemorrhage rates. In conclusion, DFL controllers are well-suited for hemorrhagic shock resuscitation and can be tuned to meet the response rates set by clinical practice guidelines for this application.

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A Proposed Fuzzy Model for Diseases Diagnosis

Cited by 3 publications

References 10 publications

Adaptive closed‐loop resuscitation controllers for hemorrhagic shock resuscitation

Adaptive closed‐loop resuscitation controllers for hemorrhagic shock resuscitation

An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic

Dual Input Fuzzy Logic Controllers for Closed Loop Hemorrhagic Shock Resuscitation

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