{"id":"https://openalex.org/W2589064887","doi":"https://doi.org/10.1109/nafips.2016.7851629","title":"A hybrid fuzzy clustering approach for fertile and unfertile analysis","display_name":"A hybrid fuzzy clustering approach for fertile and unfertile analysis","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2589064887","doi":"https://doi.org/10.1109/nafips.2016.7851629","mag":"2589064887"},"language":"en","primary_location":{"id":"doi:10.1109/nafips.2016.7851629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nafips.2016.7851629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002550655","display_name":"Shima Soltanzadeh","orcid":"https://orcid.org/0000-0002-0780-0890"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Shima Soltanzadeh","raw_affiliation_strings":["Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106234190","display_name":"Mohammad Hosein Fazel Zarandi","orcid":null},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad Hosein Fazel Zarandi","raw_affiliation_strings":["Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017333941","display_name":"Mojtaba Barzegar Astanjin","orcid":null},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mojtaba Barzegar Astanjin","raw_affiliation_strings":["Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002550655"],"corresponding_institution_ids":["https://openalex.org/I158248296"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.17911625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9509999752044678,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7423216104507446},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.707652747631073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6284801959991455},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5955801010131836},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5374157428741455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5209963917732239},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49088436365127563},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4806874692440033},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.45972388982772827},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.440351277589798},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41495904326438904},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3435513973236084},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2260189652442932},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1536790132522583}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7423216104507446},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.707652747631073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6284801959991455},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5955801010131836},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5374157428741455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5209963917732239},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49088436365127563},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4806874692440033},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.45972388982772827},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.440351277589798},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41495904326438904},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3435513973236084},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2260189652442932},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1536790132522583}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nafips.2016.7851629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nafips.2016.7851629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1566719716","https://openalex.org/W2091469633","https://openalex.org/W2123248715","https://openalex.org/W2132073332","https://openalex.org/W2140190241","https://openalex.org/W2149772057","https://openalex.org/W2157825442","https://openalex.org/W2182341367","https://openalex.org/W2320421650","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4385649027","https://openalex.org/W4400094315","https://openalex.org/W2970784617","https://openalex.org/W2126639667","https://openalex.org/W4388681644","https://openalex.org/W4313459160","https://openalex.org/W4367331014","https://openalex.org/W3160122104","https://openalex.org/W2386767720","https://openalex.org/W2066363065"],"abstract_inverted_index":{"Diagnosis":[0],"of":[1,44,59,80,121,128,133],"male":[2,81],"infertility":[3,82],"by":[4],"the":[5,19,28,38,49,57,78,88,92,100,109,119,126,134,141,160],"laboratory":[6,50],"tests":[7],"is":[8,13,166],"expensive,":[9],"and":[10,21,70,117,163],"sometimes":[11],"it":[12],"intolerable":[14],"for":[15,99],"patients.":[16],"Filling":[17],"out":[18],"questionnaire":[20],"then":[22],"using":[23,113,147],"classification":[24,61,153,170],"method":[25,98,116],"can":[26,47],"be":[27],"first":[29],"step":[30],"in":[31,37,77],"decision":[32],"making":[33],"process,":[34],"so":[35],"only":[36],"cases":[39],"with":[40],"a":[41,75,114,152,155],"high":[42],"probability":[43],"infertility,":[45],"we":[46,55,105],"use":[48],"tests.":[51],"In":[52,102],"this":[53,103,122],"paper,":[54,104],"evaluated":[56],"performance":[58,127,138,157,165],"four":[60],"methods":[62,135,171],"including":[63],"naive":[64],"Bayesian,":[65],"neural":[66],"network,":[67],"logistic":[68,173],"regression,":[69],"fuzzy":[71,148],"c-means":[72,149],"clustering":[73,150],"as":[74,151],"classification,":[76],"diagnosis":[79],"due":[83],"to":[84,159,168],"environmental":[85],"factors.":[86],"Since":[87],"data":[89],"are":[90,95],"unbalanced,":[91],"ROC":[93,161],"curves":[94,162],"most":[96,132],"suitable":[97],"comparison.":[101],"also":[106],"have":[107,144],"selected":[108],"more":[110],"important":[111],"features":[112],"filtering":[115],"examined":[118],"impact":[120],"feature":[123],"reduction":[124],"on":[125],"each":[129],"method;":[130],"generally,":[131],"had":[136],"better":[137],"after":[139],"applying":[140],"filter.":[142],"We":[143],"showed":[145],"that":[146],"has":[154],"good":[156],"according":[158],"its":[164],"comparable":[167],"other":[169],"like":[172],"regression.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}