{"id":"https://openalex.org/W7154077584","doi":"https://doi.org/10.31449/inf.v50i1.13040","title":"A Hybrid Deep Learning Framework for Cardiovascular Risk Prediction Using Temporal Embeddings, Ensemble Learning, and Bayesian Uncertainty Estimation","display_name":"A Hybrid Deep Learning Framework for Cardiovascular Risk Prediction Using Temporal Embeddings, Ensemble Learning, and Bayesian Uncertainty Estimation","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154077584","doi":"https://doi.org/10.31449/inf.v50i1.13040"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i1.13040","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i1.13040","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13040/6610","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.informatica.si/index.php/informatica/article/download/13040/6610","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059020658","display_name":"Jeena Joseph","orcid":"https://orcid.org/0000-0003-4070-5868"},"institutions":[{"id":"https://openalex.org/I4210115584","display_name":"Sacred Heart College","ror":"https://ror.org/02pttbd79","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210115584"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jeena Joseph","raw_affiliation_strings":["Marian College Kuttikkanam Autonomous, Idukki, Kerala, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Marian College Kuttikkanam Autonomous, Idukki, Kerala, India","institution_ids":["https://openalex.org/I4210115584"]}]},{"author_position":"last","author":{"id":null,"display_name":"K Kartheeban","orcid":null},"institutions":[{"id":"https://openalex.org/I98499257","display_name":"Kalasalingam Academy of Research and Education","ror":"https://ror.org/04fm2fn75","country_code":"IN","type":"education","lineage":["https://openalex.org/I98499257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K Kartheeban","raw_affiliation_strings":["Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India","institution_ids":["https://openalex.org/I98499257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41842277,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.7896999716758728,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.7896999716758728,"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"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.11559999734163284,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.00800000037997961,"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/boosting","display_name":"Boosting (machine learning)","score":0.6021999716758728},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5406000018119812},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5322999954223633},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.524399995803833},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.460999995470047},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.39660000801086426},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.3935999870300293},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.38260000944137573},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.3808000087738037},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.3732999861240387}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7724000215530396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.640500009059906},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6021999716758728},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5322999954223633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.524399995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001000165939331},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.39660000801086426},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3935999870300293},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.3732999861240387},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2558000087738037},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i1.13040","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i1.13040","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13040/6610","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.31449/inf.v50i1.13040","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i1.13040","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13040/6610","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4742666184902191,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7154077584.pdf","grobid_xml":"https://content.openalex.org/works/W7154077584.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,294],"study":[1,29],"presents":[2],"a":[3,138,142,171,187,219,226,231,268,282],"new":[4],"hybrid":[5,40,173],"deep":[6],"learning":[7],"framework":[8,54],"that":[9,304],"predicts":[10],"the":[11,28,60,80,114,122,125,131,152,166,184,255,288],"risk":[12,63,139,275,302],"of":[13,116,124,178,186,199,272,285],"cardiovascular":[14],"disease":[15],"(CVD)":[16],"by":[17],"combining":[18],"different":[19],"techniques":[20],"into":[21],"one":[22],"system.":[23],"The":[24,52,76,93,160,176,242],"methods":[25],"used":[26,58],"in":[27,65,156],"are":[30],"Long":[31],"Short-Term":[32],"Memory":[33],"(LSTM)":[34],"autoencoders":[35],"for":[36,59,100,105,307],"temporal":[37,144,161],"representation":[38],"learning,":[39,45],"feature":[41,174],"fusion,":[42],"stacked":[43,194,243],"ensemble":[44,195],"and":[46,74,89,109,148,212,218,251,290,310],"uncertainty":[47,229,271],"estimation":[48],"via":[49],"Bayesian":[50,233,265],"methods.":[51],"proposed":[53],"is":[55,305],"to":[56,67,136,169],"be":[57],"early":[61],"CVD":[62,292,301],"stratification":[64],"order":[66,135],"achieve":[68],"better":[69],"predictive":[70,270],"performance,":[71],"clinical":[72,91,309],"acceptability":[73],"interpretability.":[75],"data":[77],"source":[78],"was":[79,146,181,197,223,240],"famous":[81],"Framingham":[82],"Heart":[83],"Study":[84],"dataset":[85],"with":[86,165,246,281],"4,240":[87],"records":[88],"16":[90],"variables.":[92],"preprocessing":[94],"steps":[95],"performed":[96,245],"were":[97,163],"Hampel":[98],"filtering":[99],"outlier":[101],"removal,":[102],"mean":[103,269],"imputation":[104],"missing":[106],"value":[107],"treatment":[108],"Min-Max":[110],"normalization.":[111],"In":[112,134],"addition,":[113],"use":[115,185],"Principal":[117],"Component":[118],"Analysis":[119],"(PCA)":[120],"facilitated":[121],"retention":[123],"most":[126],"important":[127],"components":[128,168],"which":[129],"explain":[130],"highest":[132],"variance.":[133],"create":[137,170],"evolution":[140],"scenario,":[141],"synthetic":[143],"sequence":[145],"produced":[147,267],"then":[149],"passed":[150],"through":[151,183],"LSTM":[153],"autoencoder,":[154],"resulting":[155],"32-dimensional":[157],"latent":[158],"features.":[159],"embeddings":[162],"concatenated":[164],"PCA":[167],"41-dimensional":[172],"space.":[175],"problem":[177],"class":[179],"imbalance":[180],"solved":[182],"Synthetic":[188],"Minority":[189],"Over-Sampling":[190],"Technique":[191],"(SMOTE).":[192],"A":[193],"classifier":[196],"composed":[198],"eXtreme":[200],"Gradient":[201,205,213],"Boosting":[202,206,210,214],"(XGBoost),":[203],"Light":[204],"Machine":[207],"(LightGBM),":[208],"Categorical":[209],"(CatBoost),":[211],"as":[215,225],"base":[216],"learners,":[217],"Multilayer":[220],"Perceptron":[221],"(MLP)":[222],"trained":[224],"meta-learner.":[227],"For":[228],"quantification,":[230],"separate":[232],"MLP":[234],"model":[235,244,298],"using":[236],"Monte":[237],"Carlo":[238],"Dropout":[239],"created.":[241],"96.06%":[247],"accuracy,":[248],"97.67%":[249],"recall,":[250],"99.31%":[252],"Area":[253],"Under":[254],"Curve":[256],"-":[257],"Receiver":[258],"Operating":[259],"Characteristic,":[260],"thus":[261],"surpassing":[262],"single":[263],"classifiers.":[264],"analysis":[266],"0.087.":[273],"Stratified":[274],"assessment":[276],"disclosed":[277],"clinically":[278],"relevant":[279],"clusters":[280],"high":[283],"degree":[284],"correspondence":[286],"between":[287],"predicted":[289],"actual":[291],"incidence.":[293],"interpretable":[295],"concurrent":[296],"AI":[297],"provides":[299],"accurate":[300],"prediction":[303],"suitable":[306],"daily":[308],"wearable":[311],"monitoring":[312],"use.":[313]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-04-14T00:00:00"}