{"title":"Fuzzy Logic: An Easiest Technique to Predict Celiac Disease","authors":"Sunny Thukral, J. Bal","doi":"10.22232/stj.2019.07.02.11","DOIUrl":null,"url":null,"abstract":"The need for the proposed system mounts due to expensive clinical cost, the prolonged period of Genetic testing and especially painful for an individual to perform all certain clinical tests to diagnose celiac disease. With this proposed method, an individual can foretell celiac disease by just input crisp values of varied symptoms using fuzzy logic. A case study was conducted using a questionnaire procedure to obtain out the requisite symptoms in Amritsar, Punjab on 700 individuals; having 303 females and 393 males. So, the proposed system will be implemented using Mamdani Model and forms the prediction output practicing de-fuzzification when correlated with computed values produced optimum correctness. The proposed system will have a disease prediction of 96.11% accuracy according to the input values given by an individual to authenticate the celiac disease. The proposed system will provide a fruitful outcome for individuals and physicians for celiac disease disclosure in few seconds without any painful testing strategy.","PeriodicalId":22107,"journal":{"name":"Silpakorn University Science and Technology Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Silpakorn University Science and Technology Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22232/stj.2019.07.02.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The need for the proposed system mounts due to expensive clinical cost, the prolonged period of Genetic testing and especially painful for an individual to perform all certain clinical tests to diagnose celiac disease. With this proposed method, an individual can foretell celiac disease by just input crisp values of varied symptoms using fuzzy logic. A case study was conducted using a questionnaire procedure to obtain out the requisite symptoms in Amritsar, Punjab on 700 individuals; having 303 females and 393 males. So, the proposed system will be implemented using Mamdani Model and forms the prediction output practicing de-fuzzification when correlated with computed values produced optimum correctness. The proposed system will have a disease prediction of 96.11% accuracy according to the input values given by an individual to authenticate the celiac disease. The proposed system will provide a fruitful outcome for individuals and physicians for celiac disease disclosure in few seconds without any painful testing strategy.