Artificial intelligence's impact on drug delivery in healthcare supply chain management: data, techniques, analysis, and managerial implications

Journal Article

2024

Published in: https://doi.org/10.1186/s40537-024-01049-7

Healthcare supply chain management’s (HSCM) significance to economic and societal
development is huge. In today’s very competitive market, supply chains have seen
significant changes in the last several years. There is a need for technology that can
handle the increasing complexity of today’s dynamic supply chain activities. Both
machine learning (ML) and the quick dissemination of information have the potential
to revolutionize the supply chain. ML has spawned a slew of useful supply chain
applications in recent years, HSCM has received comparatively less attention. In this
study, we applied three ML algorithms such as gradient boosting (GB), histogram
gradient boosting (HGB), and cat boosting (CB) with data preprocessing tools to
predict whether the medicines are delivered on time or not in the HSCM. The data
preprocessing tools are used to manage datasets and increase the performance of
ML algorithms. There are three methods of feature selection that are applied in this
study such as Pearson correlation, chi-square test, and principal component analysis
to select the best features to push in the ML algorithms. The main results show the
CB is the best algorithm with the highest accuracy, precision, recall, and f1 score with
values respectively. The three ML algorithms are compared with other ML algorithms
to show the robustness of the applied ML algorithms. We made a sensitivity analysis
to show the chaining in learning rate (LR) and compute the accuracy of the ML
algorithms. We show the CB is not sensitive to values between 0.1 and 1.

• Publisher Name: SPRINGER NATURE
• Publishing City: Germany
• Volume Number: 11
• Issue Number: 1
• Magazine \ Newspaper: Journal of Big Data
• Pages: 1 to 25