International Journal of Nonlinear Analysis and Applications

Hybrid of particle swarm optimization algorithm and fuzzy system for diabetes diagnosis

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

2 Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran

Abstract

Diabetes is a dangerous disease in which the body is incapable of controlling blood sugar due to inadequate insulin hormone levels. This chronic disease increases blood sugar in patients. Therefore, if it is not controlled, it will cause many complications. A considerable number of people in the world suffer from this disease owing to its damage and lack of its initial diagnosis. The patient visits the doctor frequently to diagnose his/her illness and conducts various tests that are boring and costly. Increasing machine learning approaches through heuristics, and novel methods can somewhat decrease the problems. The current study aims to propose a model that can predict diabetes in patients with high accuracy. The paper introduces a new method based on the assortment of metaheuristic algorithms of a particle swarm and fuzzy inference system. The proposed method utilizes fuzzy systems to binary the particle swarm algorithm. The achieved model is applied to the diabetes dataset and then evaluated using a neural network classifier. The results indicate an increase in classification accuracy to 95.47% compared to other existing methods.

Keywords

[1] A. Afroz, M.J. Alramadan, M.N. Hossain, L. Romero, K. Alam, D.J. Magliano and B. Billah, Cost-of-illness of type 2 diabetes mellitus in low and lower-middle income countries: a systematic review, BMC Health Serv. Res. 18 (2018), no. 1, 1–10.
[2] American Diabetes Association, Diagnosis and classification of diabetes mellitus, Diabetes care 37 (2014), no. Supplement 1, S81–S90.
[3] F. Arroyave, D. Montano and F. Lizcano, Diabetes mellitus is a chronic disease that can benefit from therapy with induced pluripotent stem cells, Int. J. Molecular Sci. 21 (2020), no. 22, 8685.
[4] M.R. Bozkurt, N. Yurtay, Z. Yilmaz and C. Sertkaya, Comparison of different methods for determining diabetes, Turkish J. Electr. Eng. Comput. Sci. 22 (2014), no. 4, 1044–1055.
[5] N.G. Forouhi and N.J. Wareham, Epidemiology of diabetes, Med. 47 (2019), no. 1, 22–27.
[6] D. Gupta, A. Choudhury, U. Gupta, P. Singh and M. Prasad, Computational approach to clinical diagnosis of diabetes disease: a comparative study, Multimedia Tools Appl. 80 (2021), no. 20, 30091–30116.
[7] M.K. Hasan, M.A. Alam, D. Das, E. Hossain and M. Hasan, Diabetes prediction using ensembling of different machine learning classifiers, IEEE Access 8 (2020), 76516–76531.
[8] K. Kannadasan, D.R. Edla and V. Kuppili, Type 2 diabetes data classification using stacked autoencoders in deep neural networks, Clinic. Epidemiol. Glob. Health 7 (2019), no. 4, 530–535.
[9] J.J. Khanam and S.Y. Foo, A comparison of machine learning algorithms for diabetes prediction, ICT Express 7 (2021), no. 4, 432–439.
[10] N. Pradhan, G. Rani, V.S. Dhaka and R.C. Poonia, Diabetes prediction using artificial neural network, Deep Learning Techniques for Biomedical and Health Informatics, Academic Press, 2020.
[11] A. Singh, A. Dhillon, N. Kumar, M.S. Hossain, G. Muhammad and M. Kumar, eDiaPredict: An ensemble-based framework for diabetes prediction, ACM Trans. Multimedia Comput. Commun. Appl. 17 (2021), no. 2s, 1–26.
[12] D. Sisodia and D.S. Sisodia, Prediction of diabetes using classification algorithms, Proc. Comput. Sci. 132 (2018), 1578–1585.
[13] A. Unler and A. Murat, A discrete particle swarm optimization method for feature selection in binary classification problems, Eur. J. Oper. Res. 206 (2010), no. 3, 528–539.
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 2
February 2024
Pages 39-46
  • Receive Date: 11 July 2022
  • Revise Date: 16 March 2022
  • Accept Date: 21 December 2022