“Integration Of Generative Artificial Intelligence And The Internet Of Medical Things (Iomt): Systematic Literature Review For The 2021–2025 Period”
3rd SUSTAINABILITY, QUALITY AND AI CONFERENCE IN HEALTH SCIENCES, Thessaloniki, Yunanistan, 24 - 25 Nisan 2026, ss.1-72, (Özet Bildiri)
- Yayın Türü: Bildiri / Özet Bildiri
- Basıldığı Şehir: Thessaloniki
- Basıldığı Ülke: Yunanistan
- Sayfa Sayıları: ss.1-72
- İstanbul Kent Üniversitesi Adresli: Evet
Özet
31 A DECISION SUPPORT SYSTEM APPROACH FOR CHRONIC KIDNEY DISEASE DIAGNOSIS USING MACHINE LEARNING İlknur Sayan45, Ozan Veranyurt 46, Ülkü Veranyurt47, Didem İstafiloğlu48 ABSTRACT Purpose: Chronic kidney disease (CKD) is a progressive condition in which early detection is essential for improving patient outcomes and reducing healthcare burden. This study purpose a machine learning based approach as a clinical decision support mechanism to assist in the early identification of CKD using routinely collected patient data. Method: A benchmark dataset comprising 400 patient records with demographic, laboratory, and clinical features was utilized. A structured preprocessing pipeline was implemented to address missing values and heterogeneous data types through imputation, normalization, and encoding. Three supervised learning models, Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN) were evaluated using a stratified 80:20 train test split, with performance assessed via accuracy and F1 score. Finding: All models demonstrated strong predictive performance, with Logistic Regression achieving perfect classification (accuracy = 1.000, F1-score = 1.000). Feature analysis highlighted clinically relevant biomarkers, including hemoglobin, packed cell volume, specific gravity, and albumin, as key predictors of CKD. Conclusion: The results suggest that machine learning models, particularly when integrated into a decision support system, can support clinicians in early CKD detection and risk assessment. Such systems have the potential to enhance diagnostic accuracy, facilitate timely intervention, and improve health management processes. Keywords: Chronic Kidney Disease, Machine Learning, Classification, Medical Data Analysis, Clinical Decision Support 45 Asst. Prof. Dr., İstanbul Kent University, ilknur.sayan@kent.edu.tr, Orcid: 0000-0002-7133-5858 46 Softtech, Information Security Unit, ozan.veranyurt@softtech.com.tr, Orcid: 0000-0003-3652-2356 47 Dr., Göztepe Prof. Dr. Süleyman Yalçın City Hospital, ulkuveranyurt@gmail.com, Orcid: 0000-0003-4838-3373 48 Research Assistant, İstanbul Kent University, , didem.istafiloglu@kent.edu.tr, Orcid: 00000001-8027-9782