Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction mod...
| Published in: | Journal of Medical Systems Vol. 44; no. 9 |
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| Main Authors: | , , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
Sep2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145404953&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145404953 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2020 vid: 44 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145404953 145404953 145404953 10.1007/s10916-020-01626-2 145404953 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis. aug: au: Montano, Isabel Herrera Marques, Gonçalo Alonso, Susel Góngora López-Coronado, Miguel de la Torre Díez, Isabel affil: Department of Signal Theory and Communications, Telematics Engineering University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain sug: subj: Absenteeism Risk Assessment Persons with Disabilities Machine Learning Human Descriptive Statistics Systematic Review Neural Networks (Computer) Saudi Arabia Australia Work Capacity Evaluation Sick Leave Decision Trees Disability Evaluation ab: The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction models available in the literature. A systematic review of research papers published from 2010 to the present, related to the prediction of temporary disability and absenteeism in available in different research databases, is presented in this paper. The review focuses primarily on scientific databases such as Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate. A total of 58 articles were obtained from which, after removing duplicates and applying the search criteria, 18 have been included in the review. In total, 44% of the articles were published in 2019, representing a significant growth in scientific work regarding these indicators. This study also evidenced the interest of several countries. In addition, 56% of the articles were found to base their study on regression methods, 33% in classification, and 11% in grouping. After this systematic review, the efficiency and usefulness of artificial neural networks in predicting absenteeism and temporary incapacity are demonstrated. The studies regarding absenteeism and temporary disability at work are mainly conducted in Brazil and India, which are responsible for 44% of the analyzed papers followed by Saudi Arabia, and Australia which represented 22%. ANNs are the most used method in both classification and regression models representing 83% and 80% of the analyzed works, respectively. Only 10% of the literature use SVM, which is the less used method in regression models. Moreover, Naïve Bayes is the less used method in classification models representing 17%. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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