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

Montano, Isabel Herrera; Marques, Gonçalo; Alonso, Susel Góngora; López-Coronado, Miguel; Díez, Isabel de la Torre

doi:10.1007/s10916-020-01626-2

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…This study compared deep learning-based models with SVM model and advocated the superiority of deep learning models to predict absenteeism. Montano et al (2020) given a systematic analysis and demonstrated the effectiveness and use of neural networks for predicting absenteeism and temporary incapacity.…”

Section: Machine Learningmentioning

confidence: 99%

Predicting non-violent work behaviour among employees using machine learning techniques

Lata

Garg

2023

IJCMA

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Purpose This study aims to develop a model to predict non-violent work behaviour (NVWB) among employees using machine learning techniques. Design/methodology/approach Four machine learning techniques (Naïve Bayes, decision tree, logistic regression and ensemble learning) were used to develop a prediction model for NVWB of employees. Also, 10-fold cross-validation method was used to validate the NVWB prediction models. The confusion matrix is used to derive various performance matrices to express the predictive capability of NVWB models quantitatively. Findings The model developed using random forest technique was identified as best NVWB prediction model, as it resulted in highest true positive rate and true negative rate, thereby resulting in the highest geometric mean, balance and area under receiver operator characteristics curve. Originality/value To the best of the authors’ knowledge, this is one of the pioneer studies that used machine learning techniques to develop a predictive model of NVBW.

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Section: Machine Learningmentioning

confidence: 99%