{"id":"https://openalex.org/W7119484161","doi":"https://doi.org/10.1016/j.ibmed.2025.100339","title":"Enhancing risk prediction for diabetes, hypertension, and heart disease using SMOTE-ENN balancing with PCA and gradient boosting in healthcare AI","display_name":"Enhancing risk prediction for diabetes, hypertension, and heart disease using SMOTE-ENN balancing with PCA and gradient boosting in healthcare AI","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7119484161","doi":"https://doi.org/10.1016/j.ibmed.2025.100339"},"language":"en","primary_location":{"id":"doi:10.1016/j.ibmed.2025.100339","doi":"https://doi.org/10.1016/j.ibmed.2025.100339","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ibmed.2025.100339","pdf_url":null,"source":{"id":"https://openalex.org/S4210227372","display_name":"Intelligence-Based Medicine","issn_l":"2666-5212","issn":["2666-5212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligence-Based Medicine","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.ibmed.2025.100339","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122305700","display_name":"Tapon Paul","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I200606013","display_name":"Daffodil International University","ror":"https://ror.org/052t4a858","country_code":"BD","type":"education","lineage":["https://openalex.org/I200606013"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Tapon Paul","raw_affiliation_strings":["Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh","institution_ids":["https://openalex.org/I200606013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121696694","display_name":"Md Assaduzzaman","orcid":"https://orcid.org/0000-0002-7838-0096","observed_orcids":["https://orcid.org/0000-0002-7838-0096"]},"institutions":[{"id":"https://openalex.org/I200606013","display_name":"Daffodil International University","ror":"https://ror.org/052t4a858","country_code":"BD","type":"education","lineage":["https://openalex.org/I200606013"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Md Assaduzzaman","raw_affiliation_strings":["Faculty of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh"],"raw_orcid":"https://orcid.org/0000-0002-7838-0096","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh","institution_ids":["https://openalex.org/I200606013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092521378","display_name":"Nafiz Fahad","orcid":"https://orcid.org/0000-0002-3207-6515","observed_orcids":["https://orcid.org/0000-0002-3207-6515"]},"institutions":[{"id":"https://openalex.org/I173029219","display_name":"Multimedia University","ror":"https://ror.org/04zrbnc33","country_code":"MY","type":"education","lineage":["https://openalex.org/I173029219"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Nafiz Fahad","raw_affiliation_strings":["Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia","institution_ids":["https://openalex.org/I173029219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122322177","display_name":"Md. Jakir Hossen","orcid":"https://orcid.org/0000-0002-9978-7987","observed_orcids":["https://orcid.org/0000-0002-9978-7987"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Md Jakir Hossen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9978-7987","affiliations":[]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5122322177"],"corresponding_institution_ids":[],"apc_list":{"value":2420,"currency":"USD","value_usd":2420},"apc_paid":{"value":2420,"currency":"USD","value_usd":2420},"fwci":35.26,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.99679727,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"100339","last_page":"100339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9951000213623047,"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":0.9951000213623047,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.00419999985024333,"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/T13702","display_name":"Machine Learning in Healthcare","score":9.999999747378752e-05,"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/chronic-disease-prediction","display_name":"chronic disease prediction","score":0.8669999837875366},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"diabetes mellitus","score":0.8109999895095825},{"id":"https://openalex.org/keywords/hypertension","display_name":"hypertension","score":1.0},{"id":"https://openalex.org/keywords/heart-disease","display_name":"heart disease","score":0.9570000171661377},{"id":"https://openalex.org/keywords/smote-enn","display_name":"SMOTE-ENN","score":0.9599999785423279},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9179999828338623},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.871999979019165},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.9990000128746033},{"id":"https://openalex.org/keywords/early-detection","display_name":"early detection","score":0.6890000104904175},{"id":"https://openalex.org/keywords/medical-records","display_name":"medical records","score":0.5329999923706055},{"id":"https://openalex.org/keywords/resource-limited-settings","display_name":"resource-limited settings","score":0.6819999814033508}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6571999788284302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4860000014305115},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46459999680519104},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4309000074863434},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.36559998989105225},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3546999990940094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2890999913215637},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.ibmed.2025.100339","doi":"https://doi.org/10.1016/j.ibmed.2025.100339","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ibmed.2025.100339","pdf_url":null,"source":{"id":"https://openalex.org/S4210227372","display_name":"Intelligence-Based Medicine","issn_l":"2666-5212","issn":["2666-5212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligence-Based Medicine","raw_type":"journal-article"},{"id":"pmh:oai:shdl.mmu.edu.my:15283","doi":null,"is_oa":false,"landing_page_url":"https://shdl.mmu.edu.my/15283/1/Enhancing%20risk%20prediction%20for%20diabetes%2C%20hypertension%2C%20and%20heart%20disease%20using%20SMOTE-ENN%20balancing%20with%20PCA%20and%20gradient%20boosting%20in%20healthcare%20AI.pdf","pdf_url":"https://shdl.mmu.edu.my/15283/1/Enhancing%20risk%20prediction%20for%20diabetes%2C%20hypertension%2C%20and%20heart%20disease%20using%20SMOTE-ENN%20balancing%20with%20PCA%20and%20gradient%20boosting%20in%20healthcare%20AI.pdf","source":{"id":"https://openalex.org/S4377196753","display_name":"Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173029219","host_organization_name":"Multimedia University","host_organization_lineage":["https://openalex.org/I173029219"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1016/j.ibmed.2025.100339","doi":"https://doi.org/10.1016/j.ibmed.2025.100339","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ibmed.2025.100339","pdf_url":null,"source":{"id":"https://openalex.org/S4210227372","display_name":"Intelligence-Based Medicine","issn_l":"2666-5212","issn":["2666-5212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","doyens","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligence-Based Medicine","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9940000176429749,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"},{"score":0.4074000120162964,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320313202","display_name":"Multimedia University","ror":"https://ror.org/04zrbnc33"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1773286549","https://openalex.org/W2012035409","https://openalex.org/W2046661587","https://openalex.org/W2070654153","https://openalex.org/W2087347434","https://openalex.org/W2104960492","https://openalex.org/W2120448874","https://openalex.org/W2148143831","https://openalex.org/W2155632266","https://openalex.org/W2295598076","https://openalex.org/W2507425200","https://openalex.org/W2573280827","https://openalex.org/W2607651637","https://openalex.org/W2610135452","https://openalex.org/W2752073414","https://openalex.org/W2913509373","https://openalex.org/W2940837674","https://openalex.org/W2970243285","https://openalex.org/W3009359219","https://openalex.org/W3011484826","https://openalex.org/W3015066510","https://openalex.org/W3034654929","https://openalex.org/W3043374725","https://openalex.org/W3043924360","https://openalex.org/W3094177185","https://openalex.org/W3110698624","https://openalex.org/W3113773467","https://openalex.org/W3118658359","https://openalex.org/W3178429221","https://openalex.org/W3203003543","https://openalex.org/W4214700231","https://openalex.org/W4239510810","https://openalex.org/W4256561644","https://openalex.org/W4281825874","https://openalex.org/W4290851792","https://openalex.org/W4315618772","https://openalex.org/W4383199020","https://openalex.org/W4407626514","https://openalex.org/W4408203160","https://openalex.org/W4409427056","https://openalex.org/W4410252940","https://openalex.org/W4410301113","https://openalex.org/W4415221977"],"related_works":["https://openalex.org/W4385299462","https://openalex.org/W4297098360","https://openalex.org/W7137806902","https://openalex.org/W7133332069","https://openalex.org/W7161418446","https://openalex.org/W7133525908","https://openalex.org/W4404703818","https://openalex.org/W4416369132","https://openalex.org/W4319602700","https://openalex.org/W7222331996"],"abstract_inverted_index":{"Predicting":[0],"chronic":[1,64,279],"disease":[2],"remains":[3],"a":[4,173,182,268,289],"crucial":[5],"problem,":[6],"particularly":[7],"in":[8,77,201],"low-resource":[9],"environments":[10],"where":[11],"accurate":[12],"and":[13,30,49,55,59,95,103,109,126,154,185,271],"timely":[14],"predictions":[15,43],"are":[16],"of":[17,191,195,238,278],"utmost":[18],"importance.":[19],"Machine":[20],"learning":[21,285],"methods":[22],"do":[23],"not":[24],"adequately":[25],"generalize,":[26],"encounter":[27],"data":[28],"imbalance":[29],"computation":[31,124],"issues":[32],"when":[33,171],"used.":[34],"In":[35],"this":[36,78],"study,":[37],"an":[38,236],"improved":[39],"model":[40,145,199,253,266],"that":[41],"makes":[42],"using":[44,112,181,233],"Synthetic":[45],"minority":[46],"over-sampling":[47],"technique":[48],"edited":[50],"nearest":[51],"neighbor":[52],"balancing":[53],"techniques":[54],"Principal":[56],"Component":[57],"Analysis":[58],"Gradient":[60,149,156],"Boosting":[61,157],"to":[62,122,140,147,294],"predict":[63],"diseases":[65],"based":[66],"on":[67],"readily":[68],"available":[69],"clinical":[70,104],"profiles":[71],"is":[72,152,169,179,244,260,267],"proposed.":[73],"The":[74,198,228,240,264],"dataset":[75],"used":[76,137],"study":[79],"was":[80,120,231],"retrieved":[81],"from":[82],"provide":[83],"precise":[84],"source:":[85],"Kaggle":[86],"dataset.":[87],"It":[88,250],"contains":[89],"over":[90],"1500":[91],"anonymized":[92],"patient":[93],"records":[94],"includes":[96],"15":[97],"features":[98],"such":[99],"as":[100],"demographic,":[101],"lifestyle,":[102],"measures.":[105],"Standardized":[106],"encoding":[107],"labels,":[108],"adjusting":[110],"classes":[111],"SMOTE-ENN":[113],"have":[114,135],"been":[115,136],"completed":[116],"before":[117],"the":[118,142,155,166,186,189,202,205,247,252,275,282],"PCA":[119,151],"conducted":[121],"improve":[123],"speed":[125],"reduce":[127],"overfitting.":[128],"Decision":[129],"Tree,":[130],"Random":[131],"Forest,":[132],"LightGBM,":[133],"XGBoost":[134],"for":[138,274],"comparison":[139],"suggest":[141],"best":[143],"performing":[144],"seen":[146],"be":[148],"Boosting.":[150],"performed,":[153],"approach":[158,273],"produces":[159],"better":[160],"results.":[161],"Precision":[162,213],"measures":[163],"how":[164],"often":[165],"classification":[167],"system":[168,242,286],"correct":[170],"making":[172,254],"positive":[174],"test":[175],"result,":[176],"while":[177],"recall":[178],"determined":[180],"contingency":[183],"table,":[184],"F1":[187],"score,":[188],"possibility":[190],"modeling":[192],"outcomes":[193],"out":[194],"100":[196],"trials.":[197],"proposed":[200],"experiment":[203],"provides":[204],"following":[206],"outputs:":[207],"Accuracy":[208],"(CV:":[209,214,219,224],"99.33%,":[210],"CI:":[211,216,221,226],"98.90%\u201399.50%),":[212],"99%,":[215,220,225],"98%\u201399.5%),":[217,222],"Recall":[218],"F1-Score":[223],"98%\u201399.5%).":[227],"model\u2019s":[229],"performance":[230,243],"evaluated":[232],"cross-validation,":[234],"yielding":[235],"accuracy":[237],"98.90%.":[239],"classifying":[241],"specified":[245],"by":[246],"ROC-AUC":[248,258],"ranking.":[249],"outperforms":[251],"indefinite":[255],"projections;":[256],"its":[257],"value":[259],"greater":[261],"than":[262],"0.99.":[263],"suggested":[265,283],"robust,":[269],"interpretable,":[270],"high-precision":[272],"early":[276],"detection":[277],"conditions.":[280],"Therefore,":[281],"machine":[284],"can":[287],"deliver":[288],"considerable":[290],"promise":[291],"with":[292],"respect":[293],"creating":[295],"patient-oriented":[296],"outcomes.":[297]},"counts_by_year":[{"year":2026,"cited_by_count":9}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-01-09T00:00:00"}