{"id":"https://openalex.org/W3136398212","doi":"https://doi.org/10.1109/bigdata50022.2020.9377903","title":"Features Importance to Improve Interpretability of Chronic Kidney Disease Early Diagnosis","display_name":"Features Importance to Improve Interpretability of Chronic Kidney Disease Early Diagnosis","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136398212","doi":"https://doi.org/10.1109/bigdata50022.2020.9377903","mag":"3136398212"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5032886370","display_name":"Pedro A. Moreno-S\u00e1nchez","orcid":"https://orcid.org/0000-0003-3987-1789"},"institutions":[{"id":"https://openalex.org/I175164238","display_name":"Sein\u00e4joki University of Applied Sciences","ror":"https://ror.org/036je1b38","country_code":"FI","type":"education","lineage":["https://openalex.org/I175164238"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Pedro A. Moreno-Sanchez","raw_affiliation_strings":["School of Health Care and Social Work, Sein\u00e4joki University of Applied Sciences, Sein\u00e4joki, Finland"],"affiliations":[{"raw_affiliation_string":"School of Health Care and Social Work, Sein\u00e4joki University of Applied Sciences, Sein\u00e4joki, Finland","institution_ids":["https://openalex.org/I175164238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5032886370"],"corresponding_institution_ids":["https://openalex.org/I175164238"],"apc_list":null,"apc_paid":null,"fwci":0.4186,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.64820669,"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":"3786","last_page":"3792"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10408","display_name":"Chronic Kidney Disease and Diabetes","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2727","display_name":"Nephrology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10408","display_name":"Chronic Kidney Disease and Diabetes","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2727","display_name":"Nephrology"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9843999743461609,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8538914918899536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6914644241333008},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6624501943588257},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6441354751586914},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6216431856155396},{"id":"https://openalex.org/keywords/kidney-disease","display_name":"Kidney disease","score":0.5612446069717407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5563435554504395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5480138063430786},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.4395013749599457},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.4287189245223999},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32829976081848145},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2269182801246643}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8538914918899536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6914644241333008},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6624501943588257},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6441354751586914},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6216431856155396},{"id":"https://openalex.org/C2778653478","wikidata":"https://www.wikidata.org/wiki/Q1054718","display_name":"Kidney disease","level":2,"score":0.5612446069717407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5563435554504395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5480138063430786},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.4395013749599457},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.4287189245223999},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32829976081848145},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2269182801246643},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:dsp.tecnalia.com:11556/11330","is_oa":false,"landing_page_url":"https://hdl.handle.net/11556/11330","pdf_url":null,"source":{"id":"https://openalex.org/S4306402037","display_name":"TECNALIA Publications (Fundaci\u00f3n TECNALIA Research & Innovation)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210113430","host_organization_name":"Tecnalia","host_organization_lineage":["https://openalex.org/I4210113430"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference output"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2031941775","https://openalex.org/W2085281262","https://openalex.org/W2091886687","https://openalex.org/W2096094293","https://openalex.org/W2101234009","https://openalex.org/W2110114082","https://openalex.org/W2122147639","https://openalex.org/W2158307047","https://openalex.org/W2241668610","https://openalex.org/W2284220431","https://openalex.org/W2303784026","https://openalex.org/W2338065342","https://openalex.org/W2464795325","https://openalex.org/W2475875369","https://openalex.org/W2548466742","https://openalex.org/W2560015483","https://openalex.org/W2739500955","https://openalex.org/W2752073414","https://openalex.org/W2765995359","https://openalex.org/W2772989509","https://openalex.org/W2782804777","https://openalex.org/W2920853473","https://openalex.org/W2942796683","https://openalex.org/W3005957464","https://openalex.org/W3014082649","https://openalex.org/W3120740533","https://openalex.org/W3128452405","https://openalex.org/W4256067422","https://openalex.org/W4285719527","https://openalex.org/W4402843978","https://openalex.org/W6675354045","https://openalex.org/W6695730244","https://openalex.org/W6697923790","https://openalex.org/W6741830416","https://openalex.org/W6747832865"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4247136043","https://openalex.org/W4312407344"],"abstract_inverted_index":{"Chronic":[0],"Kidney":[1],"Disease":[2],"(CKD)":[3],"is":[4,12,109,128,161,235],"a":[5,16,32,81,98,158,168,193],"worldwide":[6],"chronic":[7],"disease":[8],"that,":[9],"if":[10],"it":[11],"recognized":[13],"late,":[14],"leads":[15],"vast":[17],"majority":[18],"of":[19,27,35,68,92,97,119,136,146,172,201,211,229,232,242],"patients":[20],"to":[21,31,43,111,130,207,213,238],"suffer":[22],"premature":[23],"mortality":[24],"and":[25,47,107,142,154,177],"quality":[26],"life":[28],"decline":[29],"due":[30],"progressive":[33],"loss":[34],"kidney":[36,50],"function.":[37],"Data":[38,69],"mining":[39,140],"classifiers":[40],"would":[41,85],"contribute":[42],"an":[44,102,133,227],"early":[45,90],"diagnosis":[46,91],"hence":[48],"preventing":[49],"severe":[51],"damage":[52],"since":[53],"subtle":[54],"patterns":[55],"in":[56,89,170,216],"CKD":[57,93,113],"indicators":[58],"can":[59],"be":[60,214],"discovered.":[61],"By":[62,95],"employing":[63],"Cross":[64],"Industry":[65],"Standard":[66],"Process":[67],"Mining":[70],"(CRISP-DM)":[71],"methodology":[72],"along":[73],"with":[74,167,189],"features":[75,212,233],"importance":[76,231],"techniques,":[77],"this":[78],"work":[79],"develops":[80],"classifier":[82,141,166,218],"model":[83,219],"that":[84],"support":[86],"healthcare":[87],"professionals":[88],"patients.":[94],"means":[96],"data":[99,104,139,148,152,191],"workflow":[100],"pipeline,":[101],"automated":[103],"transformation,":[105],"modelling":[106],"evaluation":[108],"applied":[110],"the":[112,117,137,143,147,164,180,185,199,209,217,230,240],"dataset":[114],"extracted":[115],"from":[116,192,205],"University":[118],"California":[120],"Irvine-Machine":[121],"Learning":[122],"(UCI-ML)":[123],"repository.":[124],"The":[125],"pipeline":[126],"developed":[127],"used":[129],"carry":[131],"out":[132,237],"exhaustive":[134],"search":[135],"best":[138,165],"different":[144],"parameters":[145],"preparation's":[149],"sub-stages":[150],"like":[151],"missing":[153],"feature":[155,202],"selection.":[156],"As":[157],"result,":[159],"AdaBoost":[160],"selected":[162,234,244],"as":[163],"100%":[169],"terms":[171],"accuracy,":[173],"precision,":[174],"sensivity,":[175],"specificity,":[176],"f1-score;":[178],"outperforming":[179],"classification":[181],"results":[182],"obtained":[183],"by":[184],"related":[186],"works":[187],"even":[188],"unseen":[190],"test":[194],"set.":[195],"Regarding":[196],"model's":[197,224],"interpretability,":[198],"application":[200],"selection":[203],"reduces":[204],"24":[206],"12":[208],"group":[210],"employed":[215],"developed,":[220],"achieving":[221],"more":[222],"explainable":[223],"outputs.":[225],"Furthermore,":[226],"analysis":[228],"carried":[236],"explore":[239],"relevance":[241],"each":[243],"feature.":[245]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}