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

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Abstract

Efficient production is paramount for all types of institutions, hinging upon the attainment of predefined employee targets and their subsequent outcomes. In contemporary times, a pervasive issue plaguing institutions is declining production, primarily stemming from employee absenteeism due to various reasons, ultimately eroding profitability. In our research, we spotlight organizations that rely heavily on healthcare services to bolster their bottom line, focusing on the unique case of King Abdullah University Hospital (KAUH). Drawing insights from surveys administered to nurses, we meticulously compiled a clean dataset using the OpenRefine tool. Subsequently, we harnessed the power of Machine Learning (ML) and Artificial Neural Network (ANN) techniques to construct a classification model. We judiciously assessed performance metrics such as Accuracy, Precision, and Recall to discern the most effective model. Our comparative analysis unequivocally underscored the superiority of ANN in our classification task, boasting an impressive 82% accuracy rate for predicting nurse absenteeism. This research endeavors to forecast the likelihood of nurse absenteeism in the upcoming year and elucidate the key contributing factors that elevate the risk of such occurrences. The overarching objective is to equip King Abdullah University Hospital’s decision-makers with a valuable tool to proactively mitigate absenteeism rates, elevate healthcare service quality, and elevate production levels, ultimately yielding optimal profitability.

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Data availability

The datasets used or analyzed during the study will available upon request.

References

  • Abualigah L, Al-Ajlouni YY, Daoud MS, Altalhi M, Migdady H (2024) Fake news detection using recurrent neural network based on bidirectional LSTM and glove. Soc Netw Anal Min 14(1):1–16

    Article  Google Scholar 

  • Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727

    Article  Google Scholar 

  • Alrae R, Nasir Q, Abu Talib M (2020) Develo** house of information quality framework for IoT systems. Int J Syst Assur Eng Manage 11:1294–1313

    Article  Google Scholar 

  • Asiri A, Abdullah M (2019) Employees absenteeism factors based on data analysis and classification. Spec Issue Commun Inf Technol 12(1):119–127

    Google Scholar 

  • Badubi RM (2017) A critical risk analysis of absenteeism in the work place. Marketing 2(6):32–36

    Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogram Remote Sens 114:24–31

    Article  Google Scholar 

  • Bhatti KK, Qureshi TM (2007) Impact of employee participation on job satisfaction, employee commitment and employee productivity. Int Rev Bus Res Pap 3(2):54–68

    Google Scholar 

  • Char DS, Shah NH, Magnus D (2018) Implementing machine learning in health care-addressing ethical challenges. N Engl J Med 378(11):981

    Article  Google Scholar 

  • Čikeš V, Maškarin Ribarić H, Črnjar K (2018) The determinants and outcomes of absence behavior: a systematic literature review. Soc Sci 7(8):120

    Article  Google Scholar 

  • Gangai16 KN (2014) Absenteeism at workplace: what are the factors influencing to it?

  • Gayathri T (2018) Data mining of absentee data to increase productivity. Int J Eng Tech 4(3):478–480

    Google Scholar 

  • Kamat VB, Kittur JK (2019) Devising smart strategic framework for assessment of quality in engineering education. Int J Syst Assur Eng Manage 10:1403–1428

    Article  Google Scholar 

  • Kocak MC, Bryan TG, Stevie Lynch M et al (2018) Effects of absenteeism on company productivity, efficiency, and profitability. Bus Econ Res 8(1):115–135

    Article  Google Scholar 

  • Kocakulah MC, Kelley AG, Mitchell KM, Ruggieri MP et al (2016) Absenteeism problems and costs: causes, effects and cures. Int Bus Econ Res J (IBER) 15(3):89–96

    Article  Google Scholar 

  • Mullen KJ, Rennane S (2017) Worker absenteeism and employment outcomes: a literature review, Unpublished manuscript, RAND Corporation

  • Mzili T, Mzili I, Riffi ME, Kurdi M, Ali AH, Pamucar D, Abualigah L (2024) Enhancing covid-19 vaccination and medication distribution routing strategies in rural regions of morocco: a comparative metaheuristics analysis, Inf Med Unlocked 101467

  • Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L (2024) Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environ Monit Assess 196(3):302

    Article  Google Scholar 

  • Rish I et al (2001) An empirical study of the naive bayes classifier, In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, pp 41–46

  • Shojaie AA, Kahedi E (2019) Auto parts manufacturing quality assessment using design for six sigma (DFSS), case study in Isaco company. Int J Syst Assur Eng Manage 10:35–43

    Article  Google Scholar 

  • Silpa N, Masthanamma B (2015) A study on symptoms and preventions of employee absenteeism. Int J Sci Res Publ 5(6):1–4

    Google Scholar 

  • Song Y-Y, Ying L (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130

    Google Scholar 

  • Srivastava A, Mehrotra D, Kapur P, Aggarwal AG (2020) Analytical evaluation of agile success factors influencing quality in software industry. Int J Syst Assur Eng Manage 11:247–257

    Article  Google Scholar 

  • Ticharwa M, Cope V, Murray M (2019) Nurse absenteeism: an analysis of trends and perceptions of nurse unit managers. J Nurs Manag 27(1):109–116

    Article  Google Scholar 

  • Townsend JT (1971) Theoretical analysis of an alphabetic confusion matrix. Percept Psychophys 9(1):40–50

    Article  Google Scholar 

  • van Rossum G (1995) Python reference manual, Department of Computer Science [CS] (R 9525)

  • Vardhan A, Paakaala R, Saran S, Kumara M, Mudigonda S (2018) Evaluation of clustering algorithms on absenteeism at work dataset MVSR engineering college, India. Int J Sci Res Dev 6(06):337–342

    Google Scholar 

  • Wright RE (1995) Logistic regression

  • **ao Z, Xu X, **ng H, Luo S, Dai P, Zhan D (2021) Rtfn: a robust temporal feature network for time series classification. Inf Sci 571:65–86

    Article  MathSciNet  Google Scholar 

  • **ao Z, Zhang H, Tong H, Xu X (2022) An efficient temporal network with dual self-distillation for electroencephalography signal classification, In: IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, 2022, pp 1759–1762

  • **ng H, **ao Z, Qu R, Zhu Z, Zhao B (2022) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas 71:1–12

    Google Scholar 

  • **n J, Khishe M, Zeebaree DQ, Abualigah L, Ghazal TM Adaptive habitat biogeography-based optimizer for optimizing deep cnn hyperparameters in image classification, Heliyon

  • Yen P-Y, Kellye M, Lopetegui M, Saha A, Loversidge J, Chipps EM, Gallagher-Ford L, Buck J (2018) Nurses’ time allocation and multitasking of nursing activities: a time motion study, In: AMIA annual symposium proceedings, vol 2018, American Medical Informatics Association, p 1137

  • Zhang M-L, Zhou Z-H (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank King Abdullah University Hospital (KAUH) for providing data for this work.

Funding

Not Applicable.

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Authors and Affiliations

  1. Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, 2210, Jordan

    Dalia Alzu’bi

  2. Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan

    Mwaffaq El-Heis

  3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Anas Ratib Alsoud & Laith Abualigah

  4. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia

    Laith Abualigah

  6. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  7. Applied science research center, Applied science private university, Amman, 11931, Jordan

    Laith Abualigah

  8. Tawuniya, Data Science Specialist Riyadh, Riyadh, Saudi Arabia

    Mothanna Almahmoud

Authors
  1. Dalia Alzu’bi
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  2. Mwaffaq El-Heis
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  3. Anas Ratib Alsoud
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  4. Mothanna Almahmoud
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  5. Laith Abualigah
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Correspondence to Laith Abualigah.

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Alzu’bi, D., El-Heis, M., Alsoud, A.R. et al. Classification model for reducing absenteeism of nurses at hospitals using machine learning and artificial neural network techniques. Int J Syst Assur Eng Manag 15, 3266–3278 (2024). https://doi.org/10.1007/s13198-024-02334-7

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  • DOI: https://doi.org/10.1007/s13198-024-02334-7

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