Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Ahmadi, Hossein; Gholamzadeh, Marsa; Shahmoradi, Leila; Nilashi, Mehrbakhsh; Rashvand, Pooria

doi:10.1016/j.cmpb.2018.04.013

Cited by 135 publications

(66 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The different factors such as instability in the patient, insufficient time for collecting diagnostic data, diagnostic error along with the high volume of data, the intricacy and uncertainty in the disease diagnosis process has been increased. In contrast to the real value, the fuzzy logic techniques like fuzzy expert system, fuzzy set theory, fuzzy classification, fuzzy cognitive map, rule-based fuzzy logic, weighted fuzzy rule assist along with adaptive neuro-fuzzy inference system in detecting the degree of membership for different psychiatric disorders [37]. In simple terms, for handling imprecise and incomplete data, the use of fuzzy logic, rough set and stochastic reasoning can be an added advantage.…”

Section: Future Prospectivementioning

confidence: 99%

Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Sharma¹,

Romero²

2018

EAI Endorsed Transactions on Pervasive Health and Technology

View full text Add to dashboard Cite

Section: Future Prospectivementioning

confidence: 99%

Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Sharma¹,

Romero²

2018

EAI Endorsed Transactions on Pervasive Health and Technology

View full text Add to dashboard Cite

“…In a clinical situation, an analysis physician combines a patient's medical history, clinical symptoms, physical examination and laboratory findings [10]. Due to the elusive and complex nature of clinical decisionmaking, they may be accomplished with unwanted errors [11].…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical models are essential means for demonstrating the cause and effect relationship and evaluating the evidence for decisiveness regarding infectious diseases [12]. Computer-aided methods have the potential for examining the complexity of an infectious disease dynamics [11,12]. Computational tools are essential for understanding epidemiological patterns in a disease outbreak [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Arji

Ahmadi

Nilashi

et al. 2019

Biocybernetics and Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Its fuzzy rules are different from the common fuzzy rules. Usually, the premise conditions of fuzzy rules and the consequent conclusions of fuzzy rules are fuzzy linguistic values [46]. They are shown as follows:…”

Section: Anfismentioning

confidence: 99%

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

et al. 2018

Self Cite

View full text Add to dashboard Cite

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries' sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries' sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries' sustainability performance.

show abstract

Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Cited by 135 publications

References 88 publications

Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

Contact Info

Product

Resources

About