Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.

Journal: Clinical and experimental nephrology
Published Date: Jan 15, 2025

Abstract

BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.

Authors

  • Mizuki Ohashi
    Department of General Medicine, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.
  • Yuya Ishikawa
    Department of Information Environment, Yokohama National University Graduate School of Environment and Information Sciences, Yokohama, Japan.
  • Satoshi Arai
    Department of Social Environment and Information, Yokohama National University Graduate School of Environment and Information Sciences, Yokohama, Japan.
  • Tomoharu Nagao
    Yokohama National University, Kanagawa, Japan nagao@ynu.ac.jp.
  • Kaori Kitaoka
    Shiga University of Medical Science, NCD Epidemiology Research Center, Shiga, Japan.
  • Hajime Nagasu
    Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
  • Yuichiro Yano
    Department of General Medicine, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.
  • Naoki Kashihara
    Department of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.

Keywords

External Resources

Popular Topics
Recent Journals