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Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

Isabel Herrera Montano1, Gonçalo Marques2, Susel Góngora Alonso1

  • 1Department of Signal Theory and Communications, Telematics Engineering University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.

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Summary
This summary is machine-generated.

Machine learning models, particularly artificial neural networks (ANNs), show high efficiency in predicting employee absenteeism and temporary incapacity. This review highlights ANNs as the most successful approach for these health and safety predictions.

Keywords:
AbsenteeismArtificial neural networksMachine learningTemporary disability

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Area of Science:

  • Occupational Health
  • Data Science
  • Machine Learning

Background:

  • Employee absenteeism and temporary incapacity pose significant challenges to workforce productivity and healthcare systems.
  • Predictive modeling using machine learning offers a promising approach to mitigate these issues.
  • A systematic review is needed to consolidate current knowledge on the state-of-the-art prediction models.

Purpose of the Study:

  • To systematically analyze and review the state-of-the-art in predicting absenteeism and temporary incapacity using machine learning.
  • To identify and reveal the most successful prediction models documented in scientific literature.
  • To assess the trends and geographical distribution of research in this domain.

Main Methods:

  • Systematic literature review of research papers published from 2010 to the present.
  • Searches conducted across major scientific databases including Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate.
  • Analysis of 18 selected articles based on predefined search criteria, focusing on machine learning techniques for absenteeism and temporary disability prediction.

Main Results:

  • Artificial Neural Networks (ANNs) demonstrate high efficiency and usefulness in predicting absenteeism and temporary incapacity, being the most used method (83% in classification, 80% in regression).
  • Regression methods were predominant (56% of studies), followed by classification (33%) and grouping (11%).
  • Research is concentrated in Brazil and India (44%), followed by Saudi Arabia and Australia (22%), with a notable increase in publications since 2019.

Conclusions:

  • Artificial Neural Networks are the most effective machine learning technique for predicting employee absenteeism and temporary incapacity.
  • The field has seen significant growth, indicating increasing global interest in leveraging AI for workplace health and safety.
  • Further research can build upon these findings to develop more robust and accurate predictive models.