Fuzzy Expert System For Diagnosis Research Articles

Designing and Implementing Fuzzy Expert System for Diagnosis of Psoriasis

The differential diagnosis of Erythemato-Squamous disease is a very important issue in dermatology. These diseases include dermatitis seborrhea, lichen planus, Pityriasis rosea, Chronic Dermatiticand Pityriasis rubra pilaris. The differential diagnosis of psoriasis need to spend a lot of time and costs and incorrect or delayed diagnosis will lead to risky consequences for the patient. In this regard, a fuzzy expert system was created to diagnose psoriasis at any time and in any place.Methods: In this study, an expert system is created to diagnose psoriasis based on fuzzy logic and rule-based database using MATLAB software. Also to test system the data from 190 patients with psoriasis was used among which 126 people with psoriasis were selected.Results: The accuracy, sensitivity and accuracy of the system, was obtained 96%, 97% and 95%, respectively. The area under the ROC curve was obtained 0.97 and that of Kappa test was obtained 0.92 (P < 0.001).Conclusion: Given the importance of early diagnosis of psoriasis, the use of expert systems can play an important role in preventing disease progression, reducing severe pain and costs.

Designing a general type-2 fuzzy expert system for diagnosis of depression

Depression is a common and important mental disorder that affects the quality of human life. Since people with depression are not aware of their disorder and sometimes suffer from physical symptoms such as chronic pain, refer to a physician instead of a psychologist. Hence, physician’s diagnosis is not always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis, considering two main objectives, was developed. These objectives include accuracy of the system and diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system has five steps. In the first step, we generate general type-2 membership function by using zSlices method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The proposed general type-2 expert system has been tested and evaluated to show its performance. This Intelligent system is able to diagnose depression accurately at a suitable time.

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

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.

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

A fuzzy expert system for response determining diagnosis and management movement impairments syndrome

Diagnosis is a very important aspect of medical care. Expert systems are developed to make the skills of specialists available for non-specialists. These systems simulate human thinking and performance and approach the performance of expert systems to the performance of a human expert. The purpose of this study is to develop a fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in elbow and shoulder. A fuzzy Delphi method is used to gather data related to symptoms and treatments. By knowledge acquisition, an expert system is developed. Components of the proposed system consist of a knowledge base, fuzzy inference engine, working memory, user interface and knowledge acquisition utilities. The developed system is able to diagnose 18 disorders of the elbow and the shoulder. To compare systemic diagnosis and expert diagnosis, SPSS software is used for statistical analysis. Because 26 out of 30 patients had a systemic diagnosis similar to the expert diagnosis, it can be concluded that 86.7% of systemic diagnoses are similar to expert diagnosis. In the absence of experts, this intelligent application can provide reliable diagnosis and treatment. Application of intelligent and semi-intelligent systems such as expert systems can aid the users to make decisions.

Design of fuzzy expert system for diagnosis of cardiac diseases

Background: The logical thinking of medical practitioners play significant role in decision making about diagnosis. It exhibits variation in decisions because of their approaches to deal with uncertainties and vagueness in the knowledge and information. Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems for approximate reasoning that can appropriately handle both the uncertainty and imprecision. Aims & Objective: To develop a generic fuzzy expert system framework that can be used to design specific fuzzy expert systems for particular medical domain. Material and Methods: The generic fuzzy expert system has been designed for diagnosis of cardiac diseases. The interface between visual basic and MatLab is powerful feature of the system that offers user friendly graphical user interface. Results: Need to arrive at the most accurate medical diagnosis in a timely manner is the main outcome that may reduce the further complications. A generic fuzzy expert system for the diagnosis of various heart diseases yields better result than the classic designed systems, because this system simulates the manner of an expert in true sense. Conclusion: The particular focus is on diagnosis of heart disease by employing the fuzzy logic in expert systems. The system has been designed and tested successfully. Exhaustive rule base specifically formed for almost all heart diseases ensures the accuracy to arrive at certain decision.

不妊症における診断と治療のための医療ファジィエキスパートシステム

不妊症における診断と治療のための医療ファジィエキスパートシステム