{"id":"https://openalex.org/W6940425985","doi":"https://doi.org/10.71602/tfss.2024.1183311","title":"Fuzzy Logistic Regression Analysis Using the Least Squares Method","display_name":"Fuzzy Logistic Regression Analysis Using the Least Squares Method","publication_year":2024,"publication_date":"2024-05-16","ids":{"openalex":"https://openalex.org/W6940425985","doi":"https://doi.org/10.71602/tfss.2024.1183311"},"language":"en","primary_location":{"id":"pmh:oai:doaj.org/article:6a1ad60ac4664996b26b3703d1078e77","is_oa":true,"landing_page_url":"https://doaj.org/article/6a1ad60ac4664996b26b3703d1078e77","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Transactions on Fuzzy Sets and Systems, Vol 3, Iss 2, Pp 23-36 (2024)","raw_type":"article"},"type":"article","indexed_in":["datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doaj.org/article/6a1ad60ac4664996b26b3703d1078e77","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Behdani, Zahra","orcid":"https://orcid.org/0000-0002-0029-4833"},"institutions":[{"id":"https://openalex.org/I2800704394","display_name":"Copiah-Lincoln Community College","ror":"https://ror.org/02kc2j595","country_code":"US","type":"education","lineage":["https://openalex.org/I2800704394"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Behdani, Zahra","raw_affiliation_strings":["\u06af\u0631\u0648\u0647 \u0622\u0645\u0627\u0631- \u062f\u0627\u0646\u0634\u06a9\u062f\u0647 \u0639\u0644\u0648\u0645 \u067e\u0627\u06cc\u0647- \u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u0635\u0646\u0639\u062a\u06cc \u062e\u0627\u062a\u0645 \u0627\u0644\u0627\u0646\u0628\u06cc\u0627\u0621"],"raw_orcid":"https://orcid.org/0000-0002-0029-4833","affiliations":[{"raw_affiliation_string":"\u06af\u0631\u0648\u0647 \u0622\u0645\u0627\u0631- \u062f\u0627\u0646\u0634\u06a9\u062f\u0647 \u0639\u0644\u0648\u0645 \u067e\u0627\u06cc\u0647- \u062f\u0627\u0646\u0634\u06af\u0627\u0647 \u0635\u0646\u0639\u062a\u06cc \u062e\u0627\u062a\u0645 \u0627\u0644\u0627\u0646\u0628\u06cc\u0627\u0621","institution_ids":["https://openalex.org/I2800704394"]}]},{"author_position":"last","author":{"id":null,"display_name":"Darehmiraki, Majid","orcid":"https://orcid.org/0000-0002-5136-9639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Darehmiraki, Majid","raw_affiliation_strings":["Department of Mathematics, Behbahan Khatam Alanbia University of Technology, Behbahan, Khouzestan, Iran"],"raw_orcid":"https://orcid.org/0000-0002-5136-9639","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Behbahan Khatam Alanbia University of Technology, Behbahan, Khouzestan, Iran","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800704394"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35081883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.6100999712944031,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.6100999712944031,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.09969999641180038,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10825","display_name":"Plant Pathogens and Fungal Diseases","score":0.04610000178217888,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7245000004768372},{"id":"https://openalex.org/keywords/regression-diagnostic","display_name":"Regression diagnostic","score":0.5679000020027161},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4927999973297119},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46790000796318054},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.4645000100135803},{"id":"https://openalex.org/keywords/binomial-regression","display_name":"Binomial regression","score":0.4115000069141388},{"id":"https://openalex.org/keywords/segmented-regression","display_name":"Segmented regression","score":0.3880000114440918},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.3587000072002411},{"id":"https://openalex.org/keywords/variables","display_name":"Variables","score":0.3425999879837036},{"id":"https://openalex.org/keywords/proper-linear-model","display_name":"Proper linear model","score":0.3368000090122223}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7552000284194946},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7245000004768372},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6448000073432922},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.5679000020027161},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.4645000100135803},{"id":"https://openalex.org/C114494560","wikidata":"https://www.wikidata.org/wiki/Q4914493","display_name":"Binomial regression","level":3,"score":0.4115000069141388},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40549999475479126},{"id":"https://openalex.org/C35519122","wikidata":"https://www.wikidata.org/wiki/Q3775699","display_name":"Segmented regression","level":4,"score":0.3880000114440918},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.3368000090122223},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C1883856","wikidata":"https://www.wikidata.org/wiki/Q3407463","display_name":"Fuzzy number","level":4,"score":0.3179999887943268},{"id":"https://openalex.org/C188649462","wikidata":"https://www.wikidata.org/wiki/Q2246261","display_name":"Generalized least squares","level":3,"score":0.31630000472068787},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C90157343","wikidata":"https://www.wikidata.org/wiki/Q5188196","display_name":"Cross-sectional regression","level":4,"score":0.2937000095844269},{"id":"https://openalex.org/C93698799","wikidata":"https://www.wikidata.org/wiki/Q5428730","display_name":"Factor regression model","level":5,"score":0.29109999537467957},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C127385683","wikidata":"https://www.wikidata.org/wiki/Q1475696","display_name":"Fuzzy classification","level":4,"score":0.260699987411499},{"id":"https://openalex.org/C94465730","wikidata":"https://www.wikidata.org/wiki/Q589603","display_name":"Logistic distribution","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25529998540878296},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:doaj.org/article:6a1ad60ac4664996b26b3703d1078e77","is_oa":true,"landing_page_url":"https://doaj.org/article/6a1ad60ac4664996b26b3703d1078e77","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Transactions on Fuzzy Sets and Systems, Vol 3, Iss 2, Pp 23-36 (2024)","raw_type":"article"},{"id":"doi:10.71602/tfss.2024.1183311","is_oa":true,"landing_page_url":"https://doi.org/10.71602/tfss.2024.1183311","pdf_url":null,"source":{"id":"https://openalex.org/S169411308","display_name":"Fuzzy Sets and Systems","issn_l":"0165-0114","issn":["0165-0114","1872-6801"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:doaj.org/article:6a1ad60ac4664996b26b3703d1078e77","is_oa":true,"landing_page_url":"https://doaj.org/article/6a1ad60ac4664996b26b3703d1078e77","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Transactions on Fuzzy Sets and Systems, Vol 3, Iss 2, Pp 23-36 (2024)","raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"One":[0],"of":[1,56,90,94,111,114,136,173,181,193,217,226,237,247],"the":[2,9,31,57,75,80,109,112,143,152,161,171,174,186,206,214,224,235,240,268,278,284],"most":[3],"efficient":[4],"statistical":[5],"tools":[6],"for":[7],"modeling":[8,277],"relationship":[10,32,81],"between":[11,33,82],"a":[12,63,72,83,88,102,134,245,255,261],"dependent":[13],"variable":[14,86,139],"and":[15,87,223],"several":[16],"independent":[17],"variables":[18],"is":[19,60,97,106],"regression\u200e.":[20],"\u200eIn":[21,40],"practice\u200e,":[22],"\u200eobservations":[23],"relating":[24],"to":[25,51,78,98,100,141,169,184,204,213,270,283],"one":[26,55],"or":[27,38],"more":[28],"variables\u200e,":[29,34],"\u200eor":[30],"\u200emay":[35],"be":[36],"vague":[37],"non-specific\u200e.":[39],"such":[41],"cases\u200e,":[42],"\u200eclassic":[43],"regression":[44,61,69,168,219],"methods":[45,59],"will":[46,243],"not":[47],"have":[48],"enough":[49],"capability":[50],"model":[52,70,104,142,190,266,275],"data\u200e,":[53],"\u200eand":[54,127],"alternative":[58],"in":[62,74,165,220,229,239,260,276],"fuzzy":[64,67,76,103,157,166],"environment\u200e.":[65],"\u200eThe":[66,92,264],"logistic":[68,218],"provides":[71],"framework":[73],"environment":[77],"investigate":[79],"binary":[84],"response":[85],"set":[89,135],"covariates\u200e.":[91],"purpose":[93],"this":[95,211],"paper":[96],"attempt":[99],"develop":[101],"that":[105,151],"based":[107,199],"on":[108,200],"idea":[110],"possibility":[113],"success\u200e.":[115],"\u200eThese":[116],"possibilities":[117],"are":[118,155],"characterized":[119],"{by":[120],"several}":[121],"linguistic":[122],"phrases\u200e,":[123],"\u200eincluding":[124],"low\u200e,":[125],"\u200emedium\u200e,":[126],"high\u200e,":[128],"\u200eamong":[129],"others\u200e.":[130],"\u200eNext\u200e,":[131],"\u200ewe":[132,189,242],"{use":[133],"precise":[137],"explanatory":[138],"observations":[140,228],"logarithm":[144],"transformation":[145],"of\u200e":[146],"\"\u200epossibilistic":[147],"odds.\u200e\"":[148],"\u200eWe":[149,177,209],"assume":[150],"model's":[153,207],"parameters":[154,172],"triangular":[156],"numbers.}":[158],"We":[159],"use":[160,216],"least":[162],"squares":[163],"method":[164],"linear":[167],"estimate":[170],"provided":[175],"model\u200e.":[176,187],"compute":[178],"three":[179],"types":[180],"goodness-of-fit":[182],"criteria":[183],"evaluate":[185],"\u200eUltimately\u200e,":[188],"suspected":[191],"cases":[192],"Systemic":[194],"Lupus":[195],"Erythematosus":[196],"(SLE)":[197],"disease":[198],"significant":[201],"risk":[202],"factors":[203],"identify":[205],"application\u200e.":[208],"do":[210],"due":[212],"widespread":[215],"clinical":[221,230,262],"studies":[222],"prevalence":[225,236],"ambiguous":[227,280],"diagnosis\u200e.":[231],"\u200eFurthermore\u200e,":[232],"\u200eto":[233],"assess":[234],"diabetes":[238],"community\u200e,":[241],"collect":[244],"sample":[246],"plasma":[248],"glucose":[249],"levels\u200e,":[250],"\u200emeasured":[251],"two":[252],"hours":[253],"after":[254],"meal\u200e,":[256],"\u200efrom":[257],"each":[258],"participant":[259],"survey\u200e.":[263],"proposed":[265],"has":[267],"potential":[269],"rationally":[271],"replace":[272],"an":[273],"ordinary":[274],"clinically":[279],"condition\u200e,":[281],"\u200eaccording":[282],"findings\u200e.":[285]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}