Developing a Neural Network Model for Type 2 Diabetes Detection

Noha Alsulami; Shahenda Sarhan; Miada Almasre; Wafaa Alsaggaf

doi:10.61091/jpms202413413

Author(s)

Noha Alsulami Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah,,Saudi Arabia

Shahenda Sarhan Computer Science Department, Faculty of Computers and Information Sciences, Mansoura University,Mansoura,Egypt

Miada Almasre Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah,,Saudi Arabia

Wafaa Alsaggaf Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah,,Saudi Arabia

Abstract

Worldwide, the healthcare system is greatly impacted by the changing requirements of the people. Diabetes is a long-lasting condition that can lead to serious complications if not controlled correctly. It is divided into Type 1 (TID) and Type 2 (T2D) diabetes. Research shows that almost 90% of Diabetes cases are T2D, with TID making up around 10% of all Diabetes cases. This paper suggests a Rough-Neuro classification model for identifying Type 2 Diabetes, which includes a two-stage process. The approach includes utilizing rough sets JohnsonReducer to eliminate unnecessary features or characteristics and multilayer perceptron for illness categorization. The suggested technique seeks to reduce the amount of input characteristics, which results in a reduction in the time needed to train the neural network and the storage space required. The findings show that decreasing the amount of input characteristics results in a lower neural network training time, enhances model performance, and reduces storage needs by 63%. It is worth mentioning that a smaller neural network with only seven hidden layers, trained for 1000 epochs with a learning rate of 0.01, attained the best performance, but time and storage were much decreased.

Publication Details

Page(s) 75-87

DOI 10.61091/jpms202413413

Published Journal: Journal of Pioneering Medical Sciences, Volume: 13, Issue: 4, Year: 2024

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Keywords

type 2 diabetes, Rough set, Diabetes, Diabetes, Neural Networks, Reducts, Attribute reduction

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