{"id":"https://openalex.org/W2943449690","doi":"https://doi.org/10.1145/3310986.3311024","title":"An improved prediction method for diabetes based on a feature-based least angle regression algorithm","display_name":"An improved prediction method for diabetes based on a feature-based least angle regression algorithm","publication_year":2019,"publication_date":"2019-01-25","ids":{"openalex":"https://openalex.org/W2943449690","doi":"https://doi.org/10.1145/3310986.3311024","mag":"2943449690"},"language":"en","primary_location":{"id":"doi:10.1145/3310986.3311024","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3310986.3311024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","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/A5072436255","display_name":"Shaoming Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092944","display_name":"Dalian University","ror":"https://ror.org/00g2ypp58","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092944"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoming Qiu","raw_affiliation_strings":["Communication and Network, Laboratory Dalian University, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Communication and Network, Laboratory Dalian University, Dalian, China","institution_ids":["https://openalex.org/I4210092944"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328389","display_name":"Jiahao Li","orcid":"https://orcid.org/0000-0003-1168-8229"},"institutions":[{"id":"https://openalex.org/I4210092944","display_name":"Dalian University","ror":"https://ror.org/00g2ypp58","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092944"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Li","raw_affiliation_strings":["Communication and Network, Laboratory Dalian University, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Communication and Network, Laboratory Dalian University, Dalian, China","institution_ids":["https://openalex.org/I4210092944"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101643191","display_name":"Bo Chen","orcid":"https://orcid.org/0000-0002-9689-1252"},"institutions":[{"id":"https://openalex.org/I154833797","display_name":"Lingnan Normal University","ror":"https://ror.org/01h6ecw13","country_code":"CN","type":"education","lineage":["https://openalex.org/I154833797"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Chen","raw_affiliation_strings":["College of Information Engineering, Lingnan Normal University, Zhanjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Lingnan Normal University, Zhanjiang, China","institution_ids":["https://openalex.org/I154833797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338593","display_name":"Ping Wang","orcid":"https://orcid.org/0000-0001-5759-8283"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping Wang","raw_affiliation_strings":["Beijing Kangping Technology, Co., Ltd, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Kangping Technology, Co., Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102747354","display_name":"Xiue Gao","orcid":"https://orcid.org/0009-0001-2136-2460"},"institutions":[{"id":"https://openalex.org/I154833797","display_name":"Lingnan Normal University","ror":"https://ror.org/01h6ecw13","country_code":"CN","type":"education","lineage":["https://openalex.org/I154833797"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiue Gao","raw_affiliation_strings":["College of Information Engineering, Lingnan Normal University, Zhanjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Lingnan Normal University, Zhanjiang, China","institution_ids":["https://openalex.org/I154833797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3285,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71015027,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"232","last_page":"238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9868000149726868,"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":0.9868000149726868,"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/T10351","display_name":"Liver Disease Diagnosis and Treatment","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.7118991613388062},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.6514663696289062},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5997852683067322},{"id":"https://openalex.org/keywords/segmented-regression","display_name":"Segmented regression","score":0.5921599864959717},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5919681191444397},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.579811692237854},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5428604483604431},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.5237584114074707},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5156506299972534},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48296159505844116},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4582895040512085},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4331907629966736},{"id":"https://openalex.org/keywords/variables","display_name":"Variables","score":0.4330741763114929},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4326017200946808},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.43232715129852295},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.42150577902793884},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.4109404981136322},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36866337060928345},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35865113139152527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32904213666915894},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.2313036322593689}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7118991613388062},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.6514663696289062},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5997852683067322},{"id":"https://openalex.org/C35519122","wikidata":"https://www.wikidata.org/wiki/Q3775699","display_name":"Segmented regression","level":4,"score":0.5921599864959717},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5919681191444397},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.579811692237854},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5428604483604431},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.5237584114074707},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5156506299972534},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48296159505844116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4582895040512085},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4331907629966736},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.4330741763114929},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4326017200946808},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.43232715129852295},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.42150577902793884},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.4109404981136322},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36866337060928345},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35865113139152527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32904213666915894},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.2313036322593689},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3310986.3311024","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3310986.3311024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2041963100","https://openalex.org/W2063978378","https://openalex.org/W2103697831","https://openalex.org/W2119862467","https://openalex.org/W2128728535","https://openalex.org/W2128936926","https://openalex.org/W2135046866","https://openalex.org/W2146080612","https://openalex.org/W2149380370","https://openalex.org/W2247462025","https://openalex.org/W2329140970","https://openalex.org/W2361672390","https://openalex.org/W2583673397","https://openalex.org/W2610440831","https://openalex.org/W2618405501","https://openalex.org/W2745062488","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3147115604","https://openalex.org/W1523442101","https://openalex.org/W816849645","https://openalex.org/W1986846530","https://openalex.org/W4206974938","https://openalex.org/W2800882488","https://openalex.org/W4205845910","https://openalex.org/W4210660526","https://openalex.org/W2601061511","https://openalex.org/W1608667429"],"abstract_inverted_index":{"Existing":[0],"diabetes":[1,29,66,187,193,206],"prediction":[2,30,67,188,194],"algorithms":[3,41],"have":[4],"a":[5,21,50,100,119],"number":[6],"of":[7,35,48,71,90,103,118,145,172,203],"shortcomings,":[8],"most":[9],"notably":[10],"low":[11],"accuracy":[12,171],"and":[13,42,115,169,208],"poor":[14],"generalizability.":[15],"In":[16],"this":[17],"paper,":[18],"we":[19],"propose":[20],"method":[22],"based":[23],"on":[24],"feature":[25],"weights":[26],"to":[27,57,98,110,149,180],"improve":[28],"that":[31,158],"combines":[32],"the":[33,59,81,88,91,112,126,130,136,143,151,159,162,166,170,173,182,191,201,212],"advantages":[34],"traditional":[36],"least":[37,120],"angle":[38,121],"regression":[39,68,122,127,174],"(LARS)":[40],"principal":[43,51,95],"component":[44,52,96],"analysis":[45,53,97],"(PCA)":[46],"algorithms.First":[47],"all,":[49],"algorithm":[54,160],"is":[55,74,85,108],"used":[56,109],"obtain":[58,99],"characteristic":[60,184],"independent":[61],"variables":[62,73,168,185],"found":[63],"in":[64,211],"typical":[65],"models.":[69],"Each":[70],"these":[72],"assigned":[75],"its":[76],"own":[77],"characteristics.":[78],"After":[79],"this,":[80],"original":[82],"variable":[83,92,116],"correlation":[84,107],"multiplied":[86],"by":[87,142],"weight":[89],"obtained":[93],"using":[94,135],"new":[101,106,131],"degree":[102],"correlation.":[104],"This":[105],"optimize":[111],"forward":[113],"direction":[114],"selection":[117],"solution":[123],"before":[124],"calculating":[125],"coefficients":[128],"for":[129,165,186],"model.":[132,195],"An":[133],"experiment":[134],"Pima":[137],"Indians":[138],"Diabetes":[139],"dataset":[140],"provided":[141],"University":[144],"California":[146],"was":[147,177],"performed":[148],"validate":[150],"proposed":[152],"algorithm.":[153],"The":[154],"experimental":[155],"results":[156],"show":[157],"improved":[161],"approximation":[163],"speed":[164],"dependent":[167],"coefficients.":[175],"It":[176],"also":[178],"able":[179],"select":[181],"key":[183],"whilst":[189],"simplifying":[190],"standard":[192],"Thus,":[196],"it":[197],"may":[198],"help":[199],"with":[200],"provision":[202],"more":[204],"accurate":[205],"prevention":[207],"treatment":[209],"measures":[210],"future.":[213]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}