Towards an Expert System for the Field of Neurology Based on Fuzzy Logic

Josefiok, Mirco; Sauer, Jürgen

doi:10.1007/978-3-319-24489-1_31

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The proposed model is designed to use data mining techniques such as: neural networks, decision trees and analysis of clustering patterns among others, in order to try to improve the survival of human life. In the system proposed by Josefiok and Sauer (2015), authors improved practitioners performance in certain areas in the field of neurology because of the differential diagnoses complexity using fuzzy logic. Panavaranan and Wongsawat (2013) described the pain phenomenon.…”

Section: Related Workmentioning

confidence: 99%

Behavior patterns in hormonal treatments using fuzzy logic models

et al. 2017

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Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of B J. G. Enríquez

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Section: Related Workmentioning

confidence: 99%

Behavior patterns in hormonal treatments using fuzzy logic models

et al. 2017

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“…Accordingly, finding the valid diagnosis is a lengthy process (1). Diagnosis and management of diseases are indeed a crucial task that cannot be acquired just from textbooks or classroom knowledge.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy rule-based expert system for diagnosing cystic fibrosis

Hassanzad¹,

Orooji²,

Valinejadi³

et al. 2017

Electron Physician

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BackgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.ObjectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).MethodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.ResultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.ConclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

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Towards an Expert System for the Field of Neurology Based on Fuzzy Logic

Cited by 2 publications

References 6 publications

Behavior patterns in hormonal treatments using fuzzy logic models

Behavior patterns in hormonal treatments using fuzzy logic models

A fuzzy rule-based expert system for diagnosing cystic fibrosis

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