Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network

  • Ekhlas S. Nasser Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-3892-7015
  • Faten Abd Ali Dawood Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq
Keywords: Diabetes, Multilayer Neural Network, Accuracy, Classification, Specificity

Abstract

     Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments.

The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, of which 240 are diabetic and 42 are non-diabetic patients. The model consists of three main phases.  In the first phase, two steps are applied as a pre-processing for the dataset, which include statistical analysis and missing values handling. In the second phase, feature extraction is used for diabetes Type II using three main features, reflecting measurements of three blood parameters (C. peptide, fasting Blood Sugar, and Haemoglobin A1C). Finally, classification and performance evaluation are implemented using Feed Forward Neural Network algorithm. The experimental results of the performance of the proposed model showed 98.6% accuracy for diabetes classification.

Published
2021-10-30
How to Cite
Nasser, E. S., & Dawood , F. A. A. (2021). Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network. Iraqi Journal of Science, 62(10), 3744-3758. https://doi.org/10.24996/ijs.2021.62.10.33
Section
Computer Science