AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025400081
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ORIGINAL RESEARCH ARTICLE

Hybrid log-sigmoid neural network with metaheuristic optimization for an SEIITR diabetes mellitus model

Rashid Nawaz1 ,  Saba Kainat2* ,  Muhammad Shoaib3
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1 Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
2 Department of Physics, University of Bologna, Bologna, Italy
3 AI Center, Yuan Ze University, Taoyuan, Taiwan
AIH, 025400081 https://doi.org/10.36922/AIH025400081
Received: 29 September 2025 | Revised: 16 October 2025 | Accepted: 28 October 2025 | Published online: 31 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Diabetes mellitus is a complex metabolic disorder with diverse complications, which motivates the use of advanced computational intelligence methods for modeling its nonlinear dynamics. The goal of the current research is to obtain numerical solutions to a diabetes mellitus model by employing log-sigmoid neural networks together with both local and global search strategies. For this model, the genetic algorithm (GA) serves as a global search method, while the sequential quadratic programming approach is employed as a local optimizer. In this study, the problem is addressed using a hybrid solution strategy, introducing a new aspect to the existing research on diabetes mellitus modeling. The proposed model comprises five groups: susceptible, exposed, infected without treatment, infected with treatment, and recovered individuals. To compare the reliability, precision, and consistency of the proposed technique, the log-sigmoid neural network optimized through GA and sequential quadratic programming is compared with the Adam numerical solver. An absolute error within very small ranges is achieved, demonstrating the solver’s proficiency. In addition, 100 independent trials and a network of 5 neurons were employed to test the validity of the proposed stochastic approach, together with statistical measures including root mean squared error, Theil’s inequality coefficients, and mean absolute deviation.

Keywords
Diabetes mellitus model
Log-sigmoid neural networks
Genetic algorithm
Sequential quadratic programming
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Sastry K, Goldberg D, Kendall G. Genetic algorithms. In: Burke EK, Kendall G, editors. Search Methodologies. Boston, MA: Springer; 2005. doi: 10.1007/0-387-28356-0_4

 

  1. Das M, Roy A, Maity S, Kar S, Sengupta S. Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm. Decis Mak Appl Manag Eng. 2022;5(2):329-361. doi: 10.31181/dmame181221030d

 

  1. Roy SK, De D. Genetic algorithm based internet of precision agricultural things (IopaT) for agriculture 4.0. Int Things. 2020;18:100201. doi: 10.1016/j.iot.2020.100201

 

  1. Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Acharya UR. A novel genetic algorithm based system for the scheduling of medical treatments. Expert Syst Appl. 2022;195:116464. doi: 10.1016/j.eswa.2021.116464

 

  1. Bozorgi Amiri A, Akbari M, Dadashpour I. A routing- allocation model for relief logistics with demand uncertainty: A genetic algorithm approach. J Ind Eng Manag Stud. 2022;8(2):93-110. doi: 10.22116/jiems.2022.138125

 

  1. Cassar DR, Santos GG, Zanotto ED. Designing optical glasses by machine learning coupled with a genetic algorithm. Ceram Int. 2021;47(8):10555-10564. doi: 10.1016/j.ceramint.2020.12.167

 

  1. Sang B. Application of genetic algorithm and BP neural network in supply chain finance under information sharing. J Comput Appl Math. 2021;384:113170. doi: 10.1016/j.cam.2020.113170

 

  1. Xu L, Hou L, Zhu Z, et al. Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm. Energy. 2021;222:119955. doi: 10.1016/j.energy.2021.119955

 

  1. Berahas AS, Shi J, Yi Z, Zhou B. Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction. Comput Optim Appl. 2023;86(1):79-116. doi: 10.1007/s10589-023-00483-2

 

  1. Xia L, Ling J, Xu Z, Bi R, Zhao W, Xiang S. Application of sequential quadratic programming based on active set method in cleaner production. Clean Techn Environ Policy. 2022;24(1):413-422. doi: 10.1007/s10098-021-02207-8

 

  1. Welhazi Y, Guesmi T, Alshammari BM, et al. A novel hybrid chaotic Jaya and sequential quadratic programming method for robust design of power system stabilizers and static VAR compensator. Energies. 2022;15(3):860. doi: 10.3390/en15030860

 

  1. Wang QJ. The genetic algorithm and its application to calibrating conceptual rainfall‐runoff models. Water Resour Res. 1991;27(9):2467-2471. doi: 10.1029/91WR01305

 

  1. Mehmood A, Zameer A, Ling SH, Rehman AU, Raja MA. Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming. Neural Comput Appl. 2020;32(14):10337-10357. doi: 10.1007/s00521-019-04573-3

 

  1. Ilyas H, Ahmad I, Raja MA, Shoaib M. A novel design of Gaussian WaveNets for rotational hybrid nanofluidic flow over a stretching sheet involving thermal radiation. Int Commun Heat Mass Transf. 2021;123:105196. doi: 10.1016/j.icheatmasstransfer.2021.105196

 

  1. Sabir Z, Raja MA, Wahab HA, Shoaib M, Aguilar JF. Integrated neuro‐evolution heuristic with sequential quadratic programming for second‐order prediction differential models. Numer Methods Partial Differ Equ. 2024;40(1):e22692. doi: 10.1002/num.22692

 

  1. Du YT, Rayner CK, Jones KL, Talley NJ, Horowitz M. Gastrointestinal symptoms in diabetes: Prevalence, assessment, pathogenesis, and management. Diabetes Care. 2018;41(3):627-637 doi: 10.2337/dc17-1536

 

  1. Bayani MA, Shakiba N, Bijani A, Moudi S. Depression and quality of life in patients with type 2 diabetes mellitus. Caspian J Intern Med. 2022;13(2):335-342. doi: 10.22088/cjim.13.2.3

 

  1. Alzaman N, Ali A. Obesity and diabetes mellitus in the Arab world. J Taibah Univ Med Sci. 2016;11(4):301-309. doi: 10.1016/j.jtumed.2016.03.009

 

  1. Tummanapalli SS, Wang LL, Dhanapalaratnam R, et al. Moderate-severe peripheral neuropathy in diabetes associated with an increased risk of dry eye disease. Optom Vis Sci. 2024;101(9):563-570. doi: 10.1097/OPX.0000000000002178

 

  1. Abdulrahman D, Elkhouly S, Nematalla EH, Mostafa A. Volume X Issue X (2025) 15 doi: 10.36922/AIH025400081 Effect of control of diabetes mellitus on corneal morphology. Egypt J Ophthalmol. 2022;2(2):86-98. doi: 10.21608/ejomos.2022.110391.1044

 

  1. Allen A, Iqbal Z, Green-Saxena A, et al. Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2022;10(1):e002560. doi: 10.1136/bmjdrc-2021-002560

 

  1. Marín-Peñalver JJ, Martín-Timón I, Sevillano-Collantes C, Del Cañizo-Gómez FJ. Update on the treatment of type 2 diabetes mellitus. World J Diabetes. 2016;7(17):354-395. doi: 10.4239/wjd.v7.i17.354

 

  1. De Vries JJ, Hoppenbrouwers T, Martinez-Torres C, et al. Effects of diabetes mellitus on fibrin clot structure and mechanics in a model of acute neutrophil extracellular traps (NETs) formation. Int J Mol Sci. 2020;21(19):7107. doi: 10.3390/ijms21197107

 

  1. Hajdu M, Knutsen MO, Vértes V, et al. Quality of glycemic control has significant impact on myocardial mechanics in type 1 diabetes mellitus. Sci Rep. 2022;12(1):20180. doi: 10.1038/s41598-022-24619-2

 

  1. Climie RE, Van Sloten TT, Bruno RM, et al. Macrovasculature and microvasculature at the crossroads between type 2 diabetes mellitus and hypertension. Hypertension. 2019;73(6):1138-1149. doi: 10.1161/hypertensionaha.118.11769

 

  1. Sabir Z, Raja MA, Baleanu D, Cengiz K, Shoaib M. Design of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numerically. Nonlinear Dyn. 2021;106(4):3199-3214. doi: 10.1007/s11071-021-06901-6

 

  1. Ali S, Ahmad I, Raja MA, Ahmad SU, Shoaib M. Design of evolutionary cubic spline intelligent solver for nonlinear Painlevé-I transcendent. Int J Mod Phys B. 2021;35(29):2150299. doi: 10.1142/S0217979221502994

 

  1. Umar M, Sabir Z, Raja MA, Aguilar JG, Amin F, Shoaib M. Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells. Math Comp Simul. 2021;188:241-253. doi: 10.1016/j.matcom.2021.04.008

 

  1. Cui Q, Xu C, Ou W, et al. Further study on Hopf bifurcation and hybrid control strategy in BAM neural networks concerning time delay. AIMS Math. 2024;9(5):13265-13290. doi: 10.3934/math.2024647

 

  1. Ilyas H, Raja MA, Ahmad I, Shoaib M. A novel design of Gaussian wavelet neural networks for nonlinear Falkner-Skan systems in fluid dynamics. Chin J Phys. 2021;72:386-402. doi: 10.1016/j.cjph.2021.05.012

 

  1. Raja MAZ, Shah FH, Tariq M, Ahmad I, Ahmad SU. Design of artificial neural network models optimized with sequential quadratic programming to study the dynamics of nonlinear Troesch’s problem arising in plasma physics. Neural Comput Appl. 2018;29(6):83-109. doi: 10.1007/s00521-016-2530-2

 

  1. Ahmad I, Raja MA, Ramos H, Bilal M, Shoaib M. Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically. Neural Comput Appl. 2021;33(11):5753-5769. doi: 10.1007/s00521-020-05355-y

 

  1. Fajri N, Sanusi, Asmaidi. SEIITR model for diabetes mellitus distribution in case of insulin and care factors. J Inotera. 2020;5(2):100-106. doi: 10.31572/inotera.Vol5.Iss2.2020.ID113

 

  1. Ahmad SU, Faisal F, Shoaib M, Raja MA. A new heuristic computational solver for nonlinear singular Thomas-Fermi system using evolutionary optimized cubic splines. Eur Phys J Plus. 2020;135(1):55. doi: 10.1140/epjp/s13360-019-00066-3

 

  1. Shoaib M, Abukhaled M, Kainat S, Nisar KS, Raja MA, Zubair G. Integrated neuro-evolution-based computing paradigm to study the COVID-19 transposition and severity in Romania and Pakistan. Int J Comput Intell Syst. 2022;15(1):80. doi: 10.1007/s44196-022-00133-1

 

  1. Shoaib M, Kainat S, Raja MA, Nisar KS. Design of artificial neural networks optimized through genetic algorithms and sequential quadratic programming for tuberculosis model. Waves Random Complex Media. 2025;35(5):8243-8266. doi: 10.1080/17455030.2022.2094028

 

  1. Shoaib M, Tabassum R, Raja MA, Nisar KS, Alqahtani MS, Abbas M. A design of predictive computational network for transmission model of Lassa fever in Nigeria. Results Phys. 2022;39:105713. doi: 10.1016/j.rinp.2022.105713

 

  1. Umar M, Raja MA, Sabir Z, Alwabli AS, Shoaib M. A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment. Eur Phys J Plus. 2020;135(7):565. doi: 10.1140/epjp/s13360-020-00557-8

 

  1. Sabir Z, Wahab HA. Evolutionary heuristic with Gudermannian neural networks for the nonlinear singular models of third kind. Phys Scr. 2021;96(12):125261. doi: 10.1088/1402-4896/ac3c56
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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