Hybrid AI-Based Chronic Kidney Disease Risk Prediction
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
There 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.
Açıklama
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153
Anahtar Kelimeler
Chronic Kidney Disease, Classification, Hybrid Machine Learning, Machine Learning, Statistical Features, Diagnosis, Forecasting, Laboratories, Learning algorithms, Learning systems, Nearest neighbor search, Support vector machines, Chronic kidney disease, Disease progression, Disease risks, Hybrid machine learning, Hybrid risks, Machine-learning, Risk prediction models, Risk predictions, Statistical features, Treatment process, Classification (of information)
Kaynak
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023
WoS Q Değeri
Scopus Q Değeri
N/A
