Type 2 Diabetes Prediction using Gray Wolf Optimization Algorithm


  • Aliakbar Tajari Siahmarzkooh




Disease forecasting, Gray Wolf Optimization (GWO), Diabetes, Clustering.


Background: Increasing the number of diabetic patients and the ignorance of most of these patients about
the dangers arising from it is a challenge that threatens human lives.
Materials and Method: In this paper, a new solution based on the Gray Wolf Optimization (GWO) algorithm
for predicting type 2 diabetes is presented. The main purpose of the proposed method is to increase the
accuracy of prediction and also to reduce the probability of getting stuck in local optimal points. In more
detail, the proposed method consists of two parts: 1- data preprocessing including data preparation and
noise cancellation and 2- data classification using gray wolf algorithm. The Pima Indians Diabetes dataset in
MATLAB simulation environment was used to analyze the data and compare the research results.
Results: The simulation results show that by adjusting the parameters of the gray wolf algorithm, about 6%
better prediction accuracy is obtained than other researches.
Conclusion: Also, for a more accurate evaluation of the proposed method, two other datasets have been
used for testing. The results of experiments show that the proposed model for health management in diabetes
is effective.

Author Biography

Aliakbar Tajari Siahmarzkooh

Assistant Professor, Department of Computer Science, Faculty of Sciences, Golestan University, Gorgan, Iran



How to Cite

Siahmarzkooh, A. T. . (2021). Type 2 Diabetes Prediction using Gray Wolf Optimization Algorithm. Indian Journal of Forensic Medicine & Toxicology, 15(3), 4390-4395. https://doi.org/10.37506/ijfmt.v15i3.15980