Hybrid AI-Based Chronic Kidney Disease Risk Prediction

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dc.authorscopusid58734925600en_US
dc.authorscopusid58735644300en_US
dc.authorscopusid58733826500en_US
dc.authorscopusid57828226000en_US
dc.authorscopusid56779734300en_US
dc.contributor.authorYordan, H.H.
dc.contributor.authorKarakoc, M.
dc.contributor.authorCalgici, E.
dc.contributor.authorKandaz, D.
dc.contributor.authorUcar, M.K.
dc.date.accessioned2024-08-23T16:07:34Z
dc.date.available2024-08-23T16:07:34Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153en_US
dc.description.abstractThere are various models and traditional methods for predicting the risk of chronic kidney disease(CKD), which can enhance treatment effectiveness, slow down disease progression, and reduce the risk of complications. However, these methods have limitations. In this study, a hybrid risk prediction model based on artificial intelligence is proposed to optimize the treatment process using data from individuals diagnosed with CKD. The dataset consists of 29 attributes, including medical laboratory results and patient history. Data sets created by utilizing these attributes in specific proportions were tested in classification models. By combining K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Ensemble Bagged Tree (EBT) machine learning algorithms, a Hybrid Machine Learning (HML) model was developed. The hybrid prediction model can accurately predict kidney disease risk with % 100 accuracy. Therefore, a supportive model for clinicians in the diagnosis and treatment process has been achieved. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ASYU58738.2023.10296642
dc.identifier.isbn979-835030659-0en_US
dc.identifier.scopus2-s2.0-85178303358en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ASYU58738.2023.10296642
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14732
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChronic Kidney Diseaseen_US
dc.subjectClassificationen_US
dc.subjectHybrid Machine Learningen_US
dc.subjectMachine Learningen_US
dc.subjectStatistical Featuresen_US
dc.subjectDiagnosisen_US
dc.subjectForecastingen_US
dc.subjectLaboratoriesen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectNearest neighbor searchen_US
dc.subjectSupport vector machinesen_US
dc.subjectChronic kidney diseaseen_US
dc.subjectDisease progressionen_US
dc.subjectDisease risksen_US
dc.subjectHybrid machine learningen_US
dc.subjectHybrid risksen_US
dc.subjectMachine-learningen_US
dc.subjectRisk prediction modelsen_US
dc.subjectRisk predictionsen_US
dc.subjectStatistical featuresen_US
dc.subjectTreatment processen_US
dc.subjectClassification (of information)en_US
dc.titleHybrid AI-Based Chronic Kidney Disease Risk Predictionen_US
dc.typeConference Objecten_US

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