{"id":"https://openalex.org/W7131702619","doi":"https://doi.org/10.1109/rif68108.2025.11406764","title":"Integrating Laboratory and Clinical Perspectives for AI-Based Prediction of Chronic Kidney Disease","display_name":"Integrating Laboratory and Clinical Perspectives for AI-Based Prediction of Chronic Kidney Disease","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7131702619","doi":"https://doi.org/10.1109/rif68108.2025.11406764"},"language":null,"primary_location":{"id":"doi:10.1109/rif68108.2025.11406764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rif68108.2025.11406764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Research in Computing at Feminine (RIF)","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/A5034670261","display_name":"Hiba Abdelmoum\u00e8ne","orcid":"https://orcid.org/0000-0003-1117-1385"},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Hiba Abdelmoum\u00e8ne","raw_affiliation_strings":["8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024836114","display_name":"Lynda Djakhdjakha","orcid":"https://orcid.org/0000-0001-9596-3077"},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Lynda Djakhdjakha","raw_affiliation_strings":["8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126946061","display_name":"Ilyas Guettaf","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097536","display_name":"University of Guelma","ror":"https://ror.org/00xe6p546","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210097536"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Ilyas Guettaf","raw_affiliation_strings":["8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000"],"affiliations":[{"raw_affiliation_string":"8 Mai 1945 Guelma University,Computer Science Department,Guelma,Algeria,24000","institution_ids":["https://openalex.org/I4210097536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034670261"],"corresponding_institution_ids":["https://openalex.org/I4210097536"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87720861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.3677999973297119,"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":0.3677999973297119,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.23260000348091125,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.11840000003576279,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kidney-disease","display_name":"Kidney disease","score":0.6873000264167786},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.45899999141693115},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44350001215934753},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4036000072956085},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.36399999260902405},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.36250001192092896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3610999882221222},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.3529999852180481}],"concepts":[{"id":"https://openalex.org/C2778653478","wikidata":"https://www.wikidata.org/wiki/Q1054718","display_name":"Kidney disease","level":2,"score":0.6873000264167786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5896999835968018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5037000179290771},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.492900013923645},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.45750001072883606},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.4212000072002411},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.31700000166893005},{"id":"https://openalex.org/C2987552334","wikidata":"https://www.wikidata.org/wiki/Q383126","display_name":"Chronic disease","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.2736999988555908},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C3018605307","wikidata":"https://www.wikidata.org/wiki/Q200779","display_name":"Complex disease","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C63363279","wikidata":"https://www.wikidata.org/wiki/Q5133848","display_name":"Clinical significance","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rif68108.2025.11406764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rif68108.2025.11406764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Research in Computing at Feminine (RIF)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5223888754844666,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2989324284","https://openalex.org/W3033515365","https://openalex.org/W3089959426","https://openalex.org/W3114299424","https://openalex.org/W3125584267","https://openalex.org/W3159345191","https://openalex.org/W3175343181","https://openalex.org/W3193414255","https://openalex.org/W4220734654","https://openalex.org/W4285115442","https://openalex.org/W4288446807","https://openalex.org/W4315618772","https://openalex.org/W4320710063","https://openalex.org/W4366158342","https://openalex.org/W4389239646","https://openalex.org/W4391820319","https://openalex.org/W4392777592","https://openalex.org/W4393854155","https://openalex.org/W4396845211","https://openalex.org/W4400994598","https://openalex.org/W4401179044","https://openalex.org/W4404164057","https://openalex.org/W4406890483"],"related_works":[],"abstract_inverted_index":{"Chronic":[0],"Kidney":[1],"Disease":[2],"(CKD)":[3],"is":[4,25],"a":[5,101,109],"silent":[6],"yet":[7],"progressive":[8],"condition,":[9],"often":[10],"diagnosed":[11],"only":[12],"at":[13],"advanced":[14,131],"stages":[15],"when":[16],"therapeutic":[17],"options":[18],"are":[19],"limited":[20],"and":[21,40,68,71,114,126,138,171,183],"costly.":[22],"Early":[23],"prediction":[24,48,94,158,189],"therefore":[26],"essential":[27],"to":[28],"reduce":[29],"the":[30,88,166,181],"incidence":[31],"of":[32,90,111,169,186],"end-stage":[33],"renal":[34],"disease":[35],"(ESRD),":[36],"improve":[37],"patient":[38],"outcomes,":[39],"optimize":[41],"healthcare":[42],"resources.":[43],"Existing":[44],"research":[45],"on":[46],"CKD":[47,188],"typically":[49],"follows":[50],"two":[51],"separate":[52],"paths:":[53],"(i)":[54],"laboratory-rich":[55],"datasets,":[56,75],"which":[57,76],"provide":[58],"precise":[59],"biological":[60,82,170],"indicators":[61],"but":[62,80],"suffer":[63],"from":[64],"small":[65],"sample":[66],"sizes":[67],"missing":[69],"values,":[70],"(ii)":[72],"large-scale":[73],"clinical":[74,154,172],"offer":[77],"population-level":[78],"coverage":[79],"lack":[81],"detail.":[83],"This":[84],"methodological":[85],"divide":[86],"constrains":[87],"development":[89],"comprehensive,":[91],"clinically":[92],"actionable":[93],"systems.":[95,190],"In":[96],"this":[97],"study,":[98],"we":[99],"conduct":[100],"comparative":[102],"analysis":[103],"using":[104],"both":[105,177],"dataset":[106],"types,":[107],"applying":[108],"range":[110],"machine":[112],"learning":[113,116],"deep":[115],"models.":[117],"Classical":[118],"algorithms":[119],"such":[120],"as":[121],"Logistic":[122],"Regression,":[123],"Random":[124],"Forests,":[125],"XGBoost":[127],"were":[128],"evaluated":[129],"alongside":[130],"architectures":[132],"including":[133],"Deep":[134],"Neural":[135,140],"Networks":[136,141],"(DNNs)":[137],"Convolutional":[139],"(CNNs).":[142],"The":[143],"results":[144],"show":[145],"that":[146,175],"laboratory-based":[147],"datasets":[148,155],"yield":[149],"high":[150],"diagnostic":[151],"precision,":[152],"while":[153],"support":[156],"scalable":[157],"across":[159],"diverse":[160],"populations.":[161],"Together,":[162],"these":[163],"findings":[164],"highlight":[165],"complementary":[167],"strengths":[168],"data,":[173],"demonstrating":[174],"integrating":[176],"perspectives":[178],"can":[179],"enhance":[180],"robustness":[182],"practical":[184],"relevance":[185],"AI-driven":[187]},"counts_by_year":[],"updated_date":"2026-02-27T14:28:36.762950","created_date":"2026-02-27T00:00:00"}