{"id":"https://openalex.org/W4388757548","doi":"https://doi.org/10.1109/uemcon59035.2023.10316009","title":"Supervised Learning for Non-Invasive Pre-Diabetes, Type 1 and Type 2 Diabetes Screening","display_name":"Supervised Learning for Non-Invasive Pre-Diabetes, Type 1 and Type 2 Diabetes Screening","publication_year":2023,"publication_date":"2023-10-12","ids":{"openalex":"https://openalex.org/W4388757548","doi":"https://doi.org/10.1109/uemcon59035.2023.10316009"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon59035.2023.10316009","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/uemcon59035.2023.10316009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 14th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (UEMCON)","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/A5000617260","display_name":"Kanika Sood","orcid":"https://orcid.org/0000-0002-7012-4266"},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kanika Sood","raw_affiliation_strings":["California State University, Fullerton,Department of Computer Science,Fullerton,United States","Department of Computer Science, California State University, Fullerton, Fullerton, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"California State University, Fullerton,Department of Computer Science,Fullerton,United States","institution_ids":["https://openalex.org/I142934699"]},{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, Fullerton, United States","institution_ids":["https://openalex.org/I142934699"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114100468","display_name":"Azucena Lizbeth Jimenez Martinez","orcid":null},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Azucena Lizbeth Jimenez Martinez","raw_affiliation_strings":["California State University, Fullerton,Department of Computer Science,Fullerton,United States","Department of Computer Science, California State University, Fullerton, Fullerton, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"California State University, Fullerton,Department of Computer Science,Fullerton,United States","institution_ids":["https://openalex.org/I142934699"]},{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, Fullerton, United States","institution_ids":["https://openalex.org/I142934699"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142934699"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26096437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0821","last_page":"0829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9983999729156494,"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/T10560","display_name":"Diabetes Management and Research","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10027","display_name":"Diabetes, Cardiovascular Risks, and Lipoproteins","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/type-2-diabetes","display_name":"Type 2 diabetes","score":0.7448149919509888},{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.6323588490486145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5797338485717773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5681921243667603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5649846792221069},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5199136137962341},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4778168499469757},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.43030211329460144},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.426202654838562},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.42289653420448303},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19244301319122314},{"id":"https://openalex.org/keywords/endocrinology","display_name":"Endocrinology","score":0.1169208288192749},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09716764092445374}],"concepts":[{"id":"https://openalex.org/C2777180221","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 diabetes","level":3,"score":0.7448149919509888},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.6323588490486145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5797338485717773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5681921243667603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5649846792221069},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5199136137962341},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4778168499469757},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.43030211329460144},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.426202654838562},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.42289653420448303},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19244301319122314},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.1169208288192749},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09716764092445374},{"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":"doi:10.1109/uemcon59035.2023.10316009","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/uemcon59035.2023.10316009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 14th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2159897062","https://openalex.org/W2560674852","https://openalex.org/W2753683282","https://openalex.org/W2973941913","https://openalex.org/W2974911641","https://openalex.org/W3021503072","https://openalex.org/W3173214468","https://openalex.org/W3184114351","https://openalex.org/W3216832278","https://openalex.org/W4251760621","https://openalex.org/W4254077482","https://openalex.org/W6636914306"],"related_works":["https://openalex.org/W3165582150","https://openalex.org/W4293261997","https://openalex.org/W4367336074","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W3210764983","https://openalex.org/W4367335949","https://openalex.org/W3089416646","https://openalex.org/W4380048833","https://openalex.org/W4285162676"],"abstract_inverted_index":{"In":[0],"this":[1],"research":[2],"we":[3],"present":[4],"preliminary":[5,152],"work":[6,149],"using":[7,176],"data":[8,18],"extracted":[9],"from":[10],"the":[11,29,33,68,87,106,114,118,170,203],"2014":[12],"Behavioral":[13],"Risk":[14],"Factor":[15],"Surveillance":[16],"System":[17],"published":[19],"by":[20,32,95],"The":[21,128],"Center":[22,34],"for":[23,35,108,134,156,209,216],"Disease":[24,36],"Control":[25,37],"and":[26,38,45,110,126,138,142,162,194,213,218],"Prevention.":[27],"Through":[28],"statistics":[30],"provided":[31],"Prevention":[39],"(CDC)":[40],"on":[41],"both":[42],"type":[43,46,136,139,157,159],"1":[44],"2":[47,140,211],"diabetes,":[48],"it":[49],"is":[50,53,61,131],"noticeable":[51],"there":[52],"a":[54,62,81,144,151,166],"significant":[55],"rise":[56],"in":[57,73,179],"diabetic":[58],"cases.":[59],"Diabetes":[60],"persistent":[63],"medical":[64],"condition":[65],"that":[66,77,202],"disrupts":[67],"body\u2019s":[69],"insulin":[70,109],"processing,":[71],"resulting":[72],"elevated":[74],"sugar":[75],"levels":[76],"may":[78],"lead":[79],"to":[80,116,132,154,181],"range":[82],"of":[83,99,120,169,172],"health":[84],"complications":[85],"over":[86],"long":[88],"term.":[89],"It":[90],"can":[91],"affect":[92],"individuals":[93],"socially":[94],"altering":[96],"their":[97],"quality":[98],"life,":[100],"creating":[101],"an":[102],"economic":[103],"impact":[104],"through":[105,165],"need":[107],"doctor":[111],"appointments.":[112],"Hence,":[113],"ability":[115],"predict":[117],"development":[119],"diabetes":[121,164],"would":[122],"facilitate":[123],"early":[124],"diagnosis":[125],"intervention.":[127],"main":[129],"goal":[130],"screen":[133,155],"pre-diabetes,":[135,161],"1,":[137,158],"cases":[141],"build":[143],"high-risk":[145],"identification":[146],"system.":[147],"This":[148],"presents":[150],"tackle":[153],"2,":[160],"no":[163],"comparative":[167],"analysis":[168,200],"performance":[171],"various":[173],"trained":[174],"models":[175],"supervised":[177],"learning":[178],"relation":[180],"different":[182],"sampling":[183],"techniques,":[184],"including":[185],"Random":[186,188],"Oversampling,":[187],"Undersampling,":[189],"Synthetic":[190,196],"Minority":[191],"Oversampling":[192],"(SMOTE),":[193],"Adaptive":[195],"Sampling":[197],"(ADASYN).":[198],"Our":[199],"indicates":[201],"K-nearest":[204],"neighbor":[205],"algorithm":[206],"performs":[207],"best":[208],"Type":[210,219],"prediction":[212],"Naive":[214],"Bayes":[215],"pre-diabetes":[217],"1.":[220]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
