Studies in Engineering and Technology
https://www.redfame.com/journal/index.php/set
<p><em>Studies in Engineering and Technology</em> (ISSN: 2330-2038; E-ISSN: 2330-2046) is an international, peer-reviewed, open-access journal, published by Redfame Publishing. The journal is published<strong></strong> in both <strong>print and online versions</strong>.</p><div class="WordSection1"><p>SET aims to promote excellence through dissemination of high-quality research findings, specialist knowledge, and discussion of professional issues that reflect the diversity of this field.</p></div><p>The journal accepts article submissions <strong><a href="/journal/index.php/set/about/submissions#onlineSubmissions">online</a></strong> or by <strong><a href="mailto:set@redfame.com">e-mail</a></strong>.</p>Redfame Publishingen-USStudies in Engineering and Technology2330-2038<p>Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.</p>Copyrights for articles published in Redfame journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department
https://www.redfame.com/journal/index.php/set/article/view/6372
<p>The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals. Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R<sup>2</sup>) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput.</p>Daniel AsuquoImeh UmorenFrancis OsangKingsley Attai
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2023-09-082023-09-0810111710.11114/set.v10i1.6372