{"id":"https://openalex.org/W1968940343","doi":"https://doi.org/10.1145/1947940.1948029","title":"Comparing the performance of data mining techniques for oral cancer prediction","display_name":"Comparing the performance of data mining techniques for oral cancer prediction","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W1968940343","doi":"https://doi.org/10.1145/1947940.1948029","mag":"1968940343"},"language":"en","primary_location":{"id":"doi:10.1145/1947940.1948029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1947940.1948029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 International Conference on Communication, Computing & Security - ICCCS '11","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/A5100615768","display_name":"Neha Sharma","orcid":"https://orcid.org/0000-0002-1976-7602"},"institutions":[{"id":"https://openalex.org/I4210159305","display_name":"National AIDS Research Institute","ror":"https://ror.org/05etrx234","country_code":"IN","type":"facility","lineage":["https://openalex.org/I37048141","https://openalex.org/I4210159305"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Neha Sharma","raw_affiliation_strings":["Nigdi Pradhikaran, Akurdi, Pune, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Nigdi Pradhikaran, Akurdi, Pune, Maharashtra, India","institution_ids":["https://openalex.org/I4210159305"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100615768"],"corresponding_institution_ids":["https://openalex.org/I4210159305"],"apc_list":null,"apc_paid":null,"fwci":1.4779,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87216803,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"433","last_page":"433"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.994700014591217,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.994700014591217,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.7394964694976807},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.7286478281021118},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7184391021728516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5894684791564941},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5819250345230103},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5470408797264099},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5183999538421631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4966633915901184},{"id":"https://openalex.org/keywords/malignancy","display_name":"Malignancy","score":0.4801754057407379},{"id":"https://openalex.org/keywords/data-classification","display_name":"Data classification","score":0.4528749883174896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4445473551750183},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1105099618434906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0756106972694397},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.05786439776420593}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7394964694976807},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.7286478281021118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7184391021728516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5894684791564941},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5819250345230103},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5470408797264099},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5183999538421631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4966633915901184},{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.4801754057407379},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.4528749883174896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4445473551750183},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1105099618434906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0756106972694397},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.05786439776420593},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1947940.1948029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1947940.1948029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 International Conference on Communication, Computing & Security - ICCCS '11","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.4699999988079071,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W208286617","https://openalex.org/W652328558","https://openalex.org/W1501991898","https://openalex.org/W1523293200","https://openalex.org/W1983024255","https://openalex.org/W1989319129","https://openalex.org/W2103069675","https://openalex.org/W2103239803","https://openalex.org/W2140190241","https://openalex.org/W2143608562","https://openalex.org/W2160449164","https://openalex.org/W2163598528","https://openalex.org/W2188014081","https://openalex.org/W2260087768","https://openalex.org/W2345757533","https://openalex.org/W2963426812"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2994772185","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2766585573","https://openalex.org/W1600111579"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"compare":[4],"the":[5,17,20,24,77,106,111,126],"two":[6,48],"popular":[7],"Data":[8],"Mining":[9],"techniques":[10],"to":[11],"evaluate":[12],"their":[13],"performance":[14],"in":[15,19,88,94,144],"predicting":[16,142],"malignancy":[18,114,143],"patients":[21,31],"who":[22,32],"visits":[23],"ENT":[25],"OPD.":[26],"This":[27],"study":[28],"examined":[29],"569":[30],"had":[33],"visited":[34],"a":[35],"tertiary":[36],"care":[37],"centre":[38],"during":[39],"Jan":[40],"2004":[41],"and":[42,57,73,80,91,132],"Dec":[43],"2009.":[44],"We":[45],"have":[46],"employed":[47],"data":[49,63,84,93],"mining":[50],"algorithm-":[51],"Multilayer":[52,69,95,133],"Perceptron":[53,70,96,134],"Neural":[54,71,97,135],"Network":[55,72,98,136],"Model":[56,131],"Tree":[58,74,128],"Boost":[59,75,129],"Model.":[60],"For":[61],"Training":[62],"as":[64,66,99,101,120],"well":[65,100],"validation":[67,92],"data,":[68],"indicates":[76],"same":[78],"specificity":[79],"sensitivity.":[81],"Misclassification":[82],"of":[83,113,117],"is":[85,115],"not":[86],"seen":[87,121],"both":[89,138],"training":[90],"tree":[102],"boost":[103],"model.":[104],"Also":[105],"most":[107],"important":[108],"variable":[109],"for":[110,141],"prediction":[112],"\"Presence":[116],"Lymph":[118],"Node\"":[119],"on":[122],"USG.":[123],"As":[124],"per":[125],"study,":[127],"Classification":[130],"model":[137],"are":[139],"optimal":[140],"patient.":[145]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}