A Survey on Chronic Kidney Disease Prediction Using Deep-Learning

Authors

  • P.B.Bankar  Department of Computer Engineering, S. B. Patil COE, Indapur, Maharashtra, India
  • A.A.Jadhav  Department of Computer Engineering, S. B. Patil COE, Indapur, Maharashtra, India
  • O.A.Thite  Department of Computer Engineering, S. B. Patil COE, Indapur, Maharashtra, India
  • S.M.Vetal  

Keywords:

Chronic Kidney Disease (CKD ), Deep Learning Model , Early Diagnosis , Laboratory Data, Prediction Model, Patient Outcomes

Abstract

Chronic Kidney Disease (CKD) is a global health concern characterized by the gradual deterioration of kidney function over time. Early detection and timely intervention are critical for managing CKD and preventing its progression to end stage renal disease.This abstract summarizes a research study focused on the development and evaluation of a deep learning model for predicting CKD,with the aim of improving early diagnosis and patient outcomes.The proposed deep learning model leverages a diverse dataset comprising clinical and laboratory data from a cohort of CKDPatients. This dataset includes a wide range of features,such as age, gender, blood pressure,serum create nine levels, glomerular filtration rate (GFR), and comorbid conditions . During the training phase , the deep learning model learns to identify subtle patterns and risk factors associated with CKD development and progression. Cross -validation techniques are employed to optimize hyperparameters and enhance the model’s generalization ability.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P.B.Bankar, A.A.Jadhav, O.A.Thite, S.M.Vetal, " A Survey on Chronic Kidney Disease Prediction Using Deep-Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.25-28, September-October-2023.