Title: Design of a machine learning to classify health beverages preferences for elderly people: an empirical study during COVID-19 in Thailand

Authors: Athakorn Kengpol; Jakkarin Klunngien

Addresses: Advanced Industrial Engineering Management Systems Research Center, Department of Industrial Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Thailand ' Advanced Industrial Engineering Management Systems Research Center, Department of Industrial Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Thailand

Abstract: This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO's recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people's preferences. Second, elderly people can be made aware of appropriate health beverages.

Keywords: decision support system; DSS; machine learning; elderly people; neural network; particle swarm optimisation; PSO; logistic regression; backpropagation; COVID-19; Thailand.

DOI: 10.1504/IJISE.2022.126984

International Journal of Industrial and Systems Engineering, 2022 Vol.42 No.3, pp.319 - 337

Received: 11 Sep 2020
Accepted: 03 Feb 2021
Published online: 16 Nov 2022 *

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