Yordan, H.H.Karakoc, M.Calgici, E.Kandaz, D.Ucar, M.K.2024-08-232024-08-232023979-835030659-0https://doi.org/10.1109/ASYU58738.2023.10296642https://hdl.handle.net/20.500.12684/147322023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153There 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.en10.1109/ASYU58738.2023.10296642info:eu-repo/semantics/closedAccessChronic Kidney DiseaseClassificationHybrid Machine LearningMachine LearningStatistical FeaturesDiagnosisForecastingLaboratoriesLearning algorithmsLearning systemsNearest neighbor searchSupport vector machinesChronic kidney diseaseDisease progressionDisease risksHybrid machine learningHybrid risksMachine-learningRisk prediction modelsRisk predictionsStatistical featuresTreatment processClassification (of information)Conference Object2-s2.0-85178303358N/A