Classification model for reducing absenteeism of nurses at hospitals using machine learning and artificial neural network techniques

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY

International Journal of System Assurance Engineering and Management Pub Date : 2024-04-22 DOI:10.1007/s13198-024-02334-7

Dalia Alzu'bi, M. El-Heis, A. R. Alsoud, Mothanna Almahmoud, L. Abualigah

引用次数: 0

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利用机器学习和人工神经网络技术减少医院护士缺勤的分类模型

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of System Assurance Engineering and Management ENGINEERING, MULTIDISCIPLINARY-

CiteScore

4.30

自引率

10.00%

发文量

252

期刊介绍： This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.