International Journal of

ADVANCED AND APPLIED SCIENCES

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

Frequency: 12

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 Volume 13, Issue 1 (January 2026), Pages: 13-26

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 Original Research Paper

Early diagnosis of end-stage renal disease risk in type 2 diabetes mellitus using advanced analysis of clinical laboratory data

 Author(s): 

 Raafat M. Munshi 1, 2, *, Othman Y. Alyahyawy 2, Lammar R. Munshi 3, Shashi Kant Gupta 1, 4

 Affiliation(s):

  1Department of Computer Science and Engineering, Lincoln University College, Petaling Jaya, Malaysia
  2Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
  3Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
  4Center for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-7696-0452

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2026.01.002

 Abstract

End-stage renal disease (ESRD) is a serious complication of Type 2 Diabetes Mellitus (T2DM) and has a significant negative effect on patient health. Early and accurate detection is essential but difficult to achieve in clinical settings. This study introduces an Optimized Grey Wolf Convolutional Decision Tree (OGW-ConvDT) classifier to predict the risk of ESRD by combining advanced machine learning techniques with clinical laboratory data. The model uses Z-score standardization for data normalization, Principal Component Analysis (PCA) to reduce data dimensions, and the SelectKBest method for selecting the most important features. A Convolutional Neural Network (CNN) is used to extract spatial features, and a Decision Tree (DT), optimized using the Grey Wolf (GW) algorithm, performs the final classification. The proposed method was tested on a publicly available dataset from Kaggle and achieved strong performance: precision (0.996), F1-score (0.996), recall (0.997), accuracy (0.997), AUC (0.999), specificity (0.959), log loss (0.009), and AUC-PRC (0.824). These results show that the OGW-ConvDT model performs better than traditional methods and provides an effective and reliable tool for early ESRD risk detection in T2DM patients.

 © 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

 End-stage renal disease, Type 2 diabetes, Risk prediction, Machine learning, Decision tree

 Article history

 Received 1 July 2025, Received in revised form 4 November 2025, Accepted 9 December 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Ethical considerations

This study was conducted using a publicly available and fully anonymized dataset obtained from Kaggle. All patient identifiers were removed by the data providers prior to release. All procedures were performed in accordance with relevant guidelines and regulations.

 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:

 Munshi RM, Alyahyawy OY, Munshi LR, and Gupta SK (2026). Early diagnosis of end-stage renal disease risk in type 2 diabetes mellitus using advanced analysis of clinical laboratory data. International Journal of Advanced and Applied Sciences, 13(1): 13-26

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8  Fig. 9  Fig. 10  Fig. 11  Fig. 12 

 Tables

  Table 1  Table 2  Table 3  Table 4

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