{"id":"https://openalex.org/W188460265","doi":null,"title":"Identification of Outliers in Medical Diagnostic System Using Data Mining Techniques","display_name":"Identification of Outliers in Medical Diagnostic System Using Data Mining Techniques","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W188460265","mag":"188460265"},"language":"en","primary_location":{"id":"mag:188460265","doi":null,"is_oa":false,"landing_page_url":"https://www.researchgate.net/profile/Deneshkumar_V/publication/274721695_Identification_of_Outliers_in_Medical_Diagnostic_System_Using_Data_Mining_Techniques/links/5528b86b0cf2e089a3a53589.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S2764685691","display_name":"International journal of statistics and applications","issn_l":"2168-5193","issn":["2168-5193","2168-5215"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["anvur-scientific"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International journal of statistics and applications","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"V. Deneshkumar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V. Deneshkumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002789368","display_name":"K. Senthamarai Kannan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K. Senthamarai Kannan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"M. Manikandan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M. Manikandan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4911,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.83873563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"4","issue":"6","first_page":"241","last_page":"248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6215999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6215999722480774,"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"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.326200008392334,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.011599999852478504,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.9990000128746033},{"id":"https://openalex.org/keywords/diagnosis","display_name":"diagnosis","score":0.527999997138977},{"id":"https://openalex.org/keywords/medical-databases","display_name":"medical databases","score":0.7120000123977661},{"id":"https://openalex.org/keywords/diabetes-symptoms","display_name":"diabetes symptoms","score":0.9449999928474426},{"id":"https://openalex.org/keywords/factor-analysis","display_name":"factor analysis","score":0.7429999709129333},{"id":"https://openalex.org/keywords/influencing-factors","display_name":"influencing factors","score":0.6110000014305115},{"id":"https://openalex.org/keywords/medical-data-analysis","display_name":"medical data analysis","score":0.5299999713897705},{"id":"https://openalex.org/keywords/data-preprocessing","display_name":"data preprocessing","score":0.4560000002384186}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8646669983863831},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7521631717681885},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.6990969777107239},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6635490655899048},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6541686058044434},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6041795611381531},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5144815444946289},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.491418719291687},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42369645833969116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26699644327163696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15059778094291687},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1071082055568695},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:188460265","doi":null,"is_oa":false,"landing_page_url":"https://www.researchgate.net/profile/Deneshkumar_V/publication/274721695_Identification_of_Outliers_in_Medical_Diagnostic_System_Using_Data_Mining_Techniques/links/5528b86b0cf2e089a3a53589.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S2764685691","display_name":"International journal of statistics and applications","issn_l":"2168-5193","issn":["2168-5193","2168-5215"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["anvur-scientific"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International journal of statistics and applications","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8560000061988831,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W127398482","https://openalex.org/W335157436","https://openalex.org/W1970655212","https://openalex.org/W1989638282","https://openalex.org/W1990643970","https://openalex.org/W2006533296","https://openalex.org/W2007861510","https://openalex.org/W2049058890","https://openalex.org/W2069051541","https://openalex.org/W2074937039","https://openalex.org/W2084416969","https://openalex.org/W2107554480","https://openalex.org/W2108360982","https://openalex.org/W2119315254","https://openalex.org/W2120236791","https://openalex.org/W2132996843","https://openalex.org/W2137130182","https://openalex.org/W2169217090","https://openalex.org/W3157407122","https://openalex.org/W4251402925"],"related_works":["https://openalex.org/W2111937426","https://openalex.org/W7096865891","https://openalex.org/W2766400337","https://openalex.org/W7217652303","https://openalex.org/W2510159559","https://openalex.org/W2144506528","https://openalex.org/W2950099330","https://openalex.org/W2561625117","https://openalex.org/W2117918301","https://openalex.org/W2097627964"],"abstract_inverted_index":{"The":[0,56,101,173,220,314],"outlier":[1,147,152,158,176,223,244],"detection":[2,90,177],"problem":[3,174,208],"has":[4],"important":[5,94,234],"applications":[6],"in":[7,40,72,96,195,200,225,236,332],"the":[8,30,65,69,73,77,97,112,118,137,141,149,188,232,252,276,279,303,307,324,333],"field":[9],"of":[10,19,59,76,111,122,140,151,171,175,185,222,231,240,254,266,271,278,312,317,326],"medical":[11,25,54,98],"research.":[12],"Clinical":[13],"databases":[14],"have":[15,247,288],"accumulated":[16],"large":[17,42,169],"quantities":[18],"information":[20,166],"about":[21,163],"patients":[22],"and":[23,46,67,87,115,148,193,203,292,294,328],"their":[24],"conditions.":[26],"In":[27],"this":[28,49,60],"study,":[29],"data":[31,50,80,99,123,126,130,144,153,179,211,227,237],"mining":[32,81,161,180,212],"techniques":[33],"are":[34,84,117,146,190,206,259],"used":[35],"to":[36,63,108,156,322,329],"search":[37],"for":[38,178,285,301,306],"relationships":[39,103,334],"a":[41,92,168,182,226,248,310],"clinical":[43],"database.":[44],"Relationships":[45],"patterns":[47],"within":[48],"could":[51],"provide":[52],"new":[53,165],"knowledge.":[55],"main":[57,315],"objective":[58],"paper":[61],"is":[62,91,154,162,181,229,299,321],"detect":[64,330],"outliers":[66,194,215,258],"identify":[68],"influence":[70,250],"factor":[71,318],"diabetes":[74],"symptoms":[75,110],"patient":[78],"using":[79],"techniques.":[82],"Results":[83],"illustrated":[85],"numerically":[86],"graphically.":[88],"Outlier":[89],"very":[93],"concept":[95],"analysis.":[100,256],"complex":[102],"that":[104,132,337],"appear":[105],"with":[106,136],"regard":[107],"diabetic":[109],"patient,":[113],"diagnoses":[114],"behavior":[116,139],"most":[119,233],"promising":[120],"areas":[121],"mining.":[124,159],"A":[125],"base":[127],"may":[128],"contain":[129],"objects":[131,145],"do":[133],"not":[134],"comply":[135],"general":[138],"data.":[142,172],"These":[143],"analysis":[150],"referred":[155],"as":[157,216],"Data":[160],"finding":[164],"from":[167,275],"group":[170,311],"rich":[183],"area":[184],"research":[186],"because":[187,239],"sequences":[189,196],"various":[191],"types":[192],"can":[197,246,268],"be":[198],"defined":[199],"multiple":[201],"ways":[202],"hence":[204],"there":[205],"different":[207],"formulations.":[209],"Most":[210],"methods":[213],"discard":[214],"noise":[217],"or":[218,262],"exceptions.":[219],"handling":[221],"observations":[224,245],"set":[228],"one":[230],"tasks":[235],"pre-processing":[238],"two":[241],"reasons.":[242],"First,":[243],"considerable":[249],"on":[251],"results":[253],"an":[255],"Second,":[257],"often":[260],"measurement":[261],"recording":[263],"errors,":[264],"some":[265],"them":[267],"represent":[269],"phenomena":[270],"interest,":[272],"something":[273],"significant":[274],"viewpoint":[277],"application":[280,316],"domain.":[281],"Some":[282],"classical":[283],"examples":[284],"inward":[286],"procedures":[287],"given":[289],"Hawkins":[290],"(12)":[291],"Barnett":[293],"Lewis":[295],"(2).":[296],"Factor":[297],"Analysis":[298],"useful":[300],"understanding":[302],"underlying":[304],"reasons":[305],"correlations":[308],"among":[309,335],"variables.":[313,339],"analytic":[319],"technique":[320],"reduce":[323],"number":[325],"variables":[327,336],"structure":[331],"classify":[338]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}