Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

Prediction of chronic kidney disease from 25 clinical features using machine learning

Yifan Xing * 1
1 Imperial College London

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 111-117
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yifan Xing. Prediction of chronic kidney disease from 25 clinical features using machine learning. ACE (2023) Vol. 17: 111-117. DOI: 10.54254/2755-2721/17/20230921.

Abstract

A growing number of people worldwide suffer from chronic kidney disease, with many individuals in developing countries lacking the necessary resources for treatment. Medical records often contain valuable information that can be utilized to predict the development of CKD, with machine learning algorithms proving particularly effective. In this study, the author analyzes a dataset of 250 participants with CKD and 150 participants without from 2015, utilizing various machine learning classifiers to determine the most significant characteristics and predict CKD development. The analysis reveals that serum creatinine, specific gravity, red blood cell count, and potassium are the four most relevant risk factors for CKD prediction. Based on these four factors, the author builds machine-learning models that can accurately predict CKD development from medical records. The results show that a combination of all the features in the original dataset achieves a similar level of accuracy as the four-feature models. This research has significant implications for clinical practice, providing doctors with a new tool to predict CKD in patients. By focusing on the most relevant features, such as serum creatinine, red blood cell count, specific gravity and potassium, physicians can make more informed decisions when treating patients with CKD.

Keywords

chronic kidney disease, feature ranking, machine learning

References

1. Bikbov B, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2020, 395: 709-733.

2. Bhaskar N, et al. Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease. IEEE Sensors Journal, 2021, 21(4): 4811-4818.

3. Ali S I, et al. Ensemble Feature Ranking for Cost-Based Non-Overlapping Groups: A Case Study of Chronic Kidney Disease Diagnosis in Developing Countries. IEEE Access, 2020.

4. Ebiaredoh-Mienye S A, et al. A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease. Bioengineering, 2022, 9(8): 350.

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6. Ebiaredoh-Mienye S A, et al. Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis. Electronics, 2020, 9(11): 1963.

7. Chittora P, et al. Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access, 2021, 9: 17312-17334.

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9. Sharma S, et al. Performance based evaluation of various machine learning classification techniques for chronic kidney disease diagnosis. arXiv preprint arXiv:1606.09581, 2016.

10. Motwani A, et al. Novel Machine Learning Model with Wrapper-Based Dimensionality Reduction for Predicting Chronic Kidney Disease Risk. Singapore: Springer Singapore, 2021.

11. Aruleba K, et al. Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review. Journal of Imaging, 2020, 6(10): 105.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230921
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated