Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network

Nor Azlan Othman; Muhammad Amirul Aizad Shaharul Azhar; Nor Salwa Damanhuri; Iqmal Ammar Mahadi; Mohd Hussaini Abbas; Sarah Addyani Shamsuddin; J Geoffrey Chase

doi:10.1016/j.cmpb.2023.107566

Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network

Comput Methods Programs Biomed. 2023 Jun:236:107566. doi: 10.1016/j.cmpb.2023.107566. Epub 2023 Apr 23.

Authors

Nor Azlan Othman¹, Muhammad Amirul Aizad Shaharul Azhar², Nor Salwa Damanhuri², Iqmal Ammar Mahadi², Mohd Hussaini Abbas², Sarah Addyani Shamsuddin², J Geoffrey Chase³

Affiliations

¹ Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia. Electronic address: azlan253@uitm.edu.my.
² Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
³ Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.

PMID: 37186981
DOI: 10.1016/j.cmpb.2023.107566

Abstract

Background and objective: The identification of insulinaemic pharmacokinetic parameters using the least-squares criterion approach is easily influenced by outlying data due to its sensitivity. Furthermore, the least-squares criterion has a tendency to overfit and produce incorrect results. Hence, this research proposes an alternative approach using the artificial neural network (ANN) with two hidden layers to optimize the identifying of insulinaemic pharmacokinetic parameters. The ANN is selected for its ability to avoid overfitting parameters and its faster speed in processing data.

Methods: 18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a.

Results and discussion: Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅_P = 20.73 [12.21, 28.57] mU·L·mmol^-1·min^-1 and ∅_D = 60.42 [26.85, 131.38] mU·L·mmol^-1 as compared to the linear least square method, ∅_P = 19.67 [11.81, 28.02] mU·L·mmol^-1 ·min^-1 and ∅_D = 46.21 [7.25, 116.71] mU·L·mmol^-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10^-4 L·mU^-1 ·min^-1 than the linear least square, SI = 17 × 10^-4 L·mU^-1 ·min^-1.

Conclusion: Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.

Keywords: Artificial neural network (ANN); Hidden layer; Insulinaemic pharmacokinetics; Parameter identification; Residual error.

MeSH terms

Humans
Insulin
Insulin Resistance*
Neural Networks, Computer

Substances

Insulin