International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 12, Issue 2 (February 2025), Pages: 72-79

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 Original Research Paper

Classifying chronic kidney disease using selected machine learning techniques

 Author(s): 

 Abrahem P. Anqui *

 Affiliation(s):

 College of Technology, Cebu Technological University, Cebu, Philippines

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0000-1231-569X

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2025.02.008

 Abstract

Chronic kidney disease (CKD) is a serious global health problem with high mortality rates, often due to late diagnosis. Early detection and classification are essential to improve treatment outcomes and slow disease progression. This study evaluates the performance of four machine learning algorithms—linear discriminant analysis (LDA), Naïve Bayes, C4.5 decision tree, and Random Forest—in classifying CKD using a Kaggle dataset containing 1,659 instances and 52 features, covering demographic, lifestyle, and clinical data. After data pre-processing, the classification accuracies of the algorithms were assessed. LDA showed the highest accuracy at 92.8%, followed by Naïve Bayes (92.1%), C4.5 (92.0%), and Random Forest (91.9%) before hyperparameter tuning. After tuning, C4.5 achieved the highest accuracy of 92.5%, followed by Random Forest (92.2%), with Naïve Bayes remaining at 92.1%. However, even after tuning, LDA remained the most accurate, demonstrating superior performance. The key features contributing to CKD classification were serum creatinine, glomerular filtration rate (GFR), muscle cramps, protein in urine, fasting blood sugar, itching, systolic blood pressure, blood urea nitrogen (BUN), HbA1c, edema, total cholesterol, body mass index (BMI), and gender. These findings confirm that LDA outperforms other algorithms in CKD classification without the need for tuning, emphasizing the value of machine learning in improving early diagnosis and management of CKD.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Chronic kidney disease, Machine learning algorithms, Early diagnosis, Classification accuracy, Key clinical features

 Article history

 Received 19 September 2024, Received in revised form 7 January 2025, Accepted 22 January 2025

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Anqui AP (2025). Classifying chronic kidney disease using selected machine learning techniques. International Journal of Advanced and Applied Sciences, 12(2): 72-79

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

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