LGPR Publication Success in International Journal of Advanced and Applied Sciences by Dr. Raafat Mohammed Munshi

#ESCI #SCOPUS #publicationsuccess #LGPR

Dr. Raafat Mohammed Munshi, LGPR Candidate published in the Journal of Advanced and Applied sciences.

Journal of Advanced and Applied sciences, 13, 1 (January 2026), Pages: 13-26 |

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Cite Score: 1.0

International Journal of ADVANCED AND APPLIED SCIENCES (IJAAS), a scientific broadcasting organization and media, provides an international medium for the communication of original research, ideas and developments in all areas of the field of Applied Sciences. IJAAS tries to apply existing scientific knowledge to develop more practical applications like technology or inventions to solve immediate, real-life problems in a scientific manner. Its main scope is application of scientific knowledge transferred into a physical environment which embraces branches of applied science such as: Engineering, Applied mathematics, Applied physics, Medicine, and Computer science.

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

https://doi.org/10.21833/ijaas.2025.12.010

Raafat M. Munshi

Lincoln University College, Petaling Jaya, Malaysia.

Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

Othman Y. Alyahyawy

Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.

Lammar R. Munshi

Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

Shashi Kant Gupta

Lincoln University College, Petaling Jaya 47810, Malaysia.

Center for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India.

Abstract

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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.

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Evaluation of the ESRD prediction model using a confusion matrix