{"id":"https://openalex.org/W2997463986","doi":"https://doi.org/10.1145/3364836.3364882","title":"A Comparison of Logistic Regression, Random Forest Models in Predicting the Risk of Diabetes","display_name":"A Comparison of Logistic Regression, Random Forest Models in Predicting the Risk of Diabetes","publication_year":2019,"publication_date":"2019-08-24","ids":{"openalex":"https://openalex.org/W2997463986","doi":"https://doi.org/10.1145/3364836.3364882","mag":"2997463986"},"language":"en","primary_location":{"id":"doi:10.1145/3364836.3364882","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101664108","display_name":"Baoxin Zhang","orcid":"https://orcid.org/0000-0001-8156-0605"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoxin Zhang","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072929639","display_name":"Li Lu","orcid":"https://orcid.org/0000-0001-7904-8821"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Lu","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035206957","display_name":"Jiaqi Hou","orcid":"https://orcid.org/0000-0002-7363-7753"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Hou","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24185976"],"apc_list":null,"apc_paid":null,"fwci":1.0146,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7745866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"231","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9911999702453613,"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":0.9911999702453613,"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/T10027","display_name":"Diabetes, Cardiovascular Risks, and Lipoproteins","score":0.9871000051498413,"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/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.7943347692489624},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7799617648124695},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.7500792741775513},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5195029973983765},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.43100687861442566},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4179513156414032},{"id":"https://openalex.org/keywords/environmental-health","display_name":"Environmental health","score":0.38877394795417786},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.34076792001724243},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.3257756233215332},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17830204963684082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12055471539497375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.11572617292404175},{"id":"https://openalex.org/keywords/endocrinology","display_name":"Endocrinology","score":0.10753175616264343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0814422070980072}],"concepts":[{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.7943347692489624},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7799617648124695},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.7500792741775513},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5195029973983765},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.43100687861442566},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4179513156414032},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.38877394795417786},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.34076792001724243},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.3257756233215332},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17830204963684082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12055471539497375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.11572617292404175},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.10753175616264343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0814422070980072},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3364836.3364882","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3364836.3364882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third International Symposium on Image Computing and Digital Medicine","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.9200000166893005}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2095901165","https://openalex.org/W4400694452"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W4367335965","https://openalex.org/W4385574838","https://openalex.org/W4367335937","https://openalex.org/W4308573183","https://openalex.org/W3191198889","https://openalex.org/W4399767560","https://openalex.org/W4385216705"],"abstract_inverted_index":{"Diabetes":[0,44],"mellitus":[1,22,33,100,177],"has":[2,34],"become":[3],"one":[4],"of":[5,20,31,66,89,145,164,175,190,218,225],"the":[6,12,17,24,29,64,69,87,111,119,191,197,206,209,216,223],"most":[7],"harmful":[8],"chronic":[9],"diseases":[10],"in":[11,23,136,140],"world,":[13],"and":[14,83,92,101,170,184,203,221],"China":[15],"is":[16,45,212],"largest":[18],"country":[19],"diabetes":[21,32,76,90,99,176],"world.":[25],"In":[26,116],"recent":[27],"years,":[28],"prevalence":[30,65],"increased":[35],"year":[36],"by":[37,195],"year,":[38],"which":[39],"seriously":[40],"threatens":[41],"human":[42],"health.":[43],"a":[46,125,137],"chronic,":[47],"lifelong":[48],"disease.":[49],"Therefore,":[50],"early":[51,91],"preventive":[52],"measures":[53],"for":[54,72,110],"high-risk":[55,74],"diabetic":[56],"patients":[57,84],"are":[58,106],"an":[59],"effective":[60],"way":[61],"to":[62,85,118,133,214],"control":[63],"diabetes.":[67],"As":[68],"preferred":[70],"tool":[71],"screening":[73],"population,":[75],"risk":[77,88],"prediction":[78,173,188,220],"model":[79,174,192,219],"can":[80],"help":[81],"doctors":[82],"identify":[86],"take":[93],"timely":[94],"action.":[95],"Preventing":[96],"or":[97],"delaying":[98],"its":[102],"complications,":[103],"however,":[104],"there":[105],"few":[107],"predictive":[108],"models":[109],"Chinese":[112],"population":[113],"at":[114],"present.":[115],"response":[117],"above":[120],"problems,":[121],"this":[122],"study":[123],"collected":[124],"medical":[126],"examination":[127],"data":[128],"set":[129],"from":[130],"September":[131],"2014":[132],"August":[134],"2018":[135],"third-tier":[138],"hospital":[139],"Sichuan":[141],"Province.":[142],"A":[143],"total":[144,161],"22175":[146],"samples":[147],"were":[148],"collected.":[149],"The":[150,172,187],"variables":[151],"included":[152],"gender,":[153],"age,":[154],"high":[155],"density":[156,159],"lipoprotein,":[157,160],"low":[158],"cholesterol,":[162],"history":[163],"hypertension,":[165],"smoking,":[166],"alcohol":[167],"consumption,":[168],"BMI":[169],"triglyceride.":[171],"was":[178,193],"established":[179],"based":[180],"on":[181],"Logistic":[182],"regression":[183],"random":[185],"forest.":[186],"effect":[189],"compared":[194],"calculating":[196],"area":[198],"under":[199],"ROC":[200],"curve,":[201],"sensitivity":[202],"accuracy.":[204],"At":[205],"same":[207],"time,":[208],"cross-validation":[210],"method":[211],"used":[213],"improve":[215],"accuracy":[217],"solve":[222],"problem":[224],"over-fitting.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}