An approach for predicting employee churn by using data mining

Yigit, Ibrahim Onuralp; Shourabizadeh, Hamed

doi:10.1109/idap.2017.8090324

Cited by 42 publications

(21 citation statements)

References 10 publications

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“…The confusion matrix results ( Table 4 ) and ROC curve ( Fig. 6 ) demonstrated that the PST-NN model was highly efficient, in terms of psychosocial risk classification, as compared to other experiments and models ( Larrabee et al, 2003 ; Baradaran, Ghadami & Malihi, 2008 ; Aliabadi, Farhadian & Darvishi, 2015 ; Farhadian, Aliabadi & Darvishi, 2015 ; Yigit & Shourabizadeh, 2017 ; Jebelli, Khalili & Lee, 2019 ). The level of precision and low error percentage of PST-NN approach demonstrated the ease adaptation of the mathematical structure to the input variables, generating a model that can be used to perform preventive interventions in occupational health by way of prediction, based on psychosocial, physiological, and musculoskeletal factors.…”

Section: Discussionmentioning

confidence: 85%

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Navarro

Castrillón

Osorio

et al. 2021

PeerJ Computer Science

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Background Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs. Methods The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. Results The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. Conclusions The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it.

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Section: Discussionmentioning

confidence: 85%

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Navarro

Castrillón

Osorio

et al. 2021

PeerJ Computer Science

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“…Studies showed that companies should reassess their adopted working conditions and salary policies, which were some of the main reasons for employees deciding to look for jobs somewhere else. Increasing overall satisfaction rates in the workplace was shown to directly influence productivity outcomes [20], thus, fostering feelings of motivation, engagement, and commitment to the organization and the work performed, consequently reduced absenteeism rates and a desire to leave and change organizations in the pursuit of better living conditions [22].…”

Section: Resultsmentioning

confidence: 99%

The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization

Pirrolas

Correia

2021

Sustainability

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Given the current socio-economic context in which the labor market is set in, if we were to consider both employment opportunities in specific economic periods and the individual expectations workers have regarding one’s working conditions, it should be a matter of one’s individual right of choice to decide whether or not to stay or leave and change companies. The paper we present before you took into account the phenomenon of churning as a cyclical process. Our main goal was to understand the main causes leading to it in the context of human resources and, ultimately, what were the consequences emerging from it. In order to carry out this analysis, we put forward a conceptual-theoretical model of the phenomenon of churning, made possible through the analysis of both the currently available literature and the empirical studies and conclusions stemming from the latter. The aforementioned analysis allowed us to trace all main causes responsible for the origin of churning and also, to understand how it could be implemented further on, in order to minimize the costs associated with hiring new personnel and retaining qualified employees by resorting beforehand to the implementation of strategic measures of human resources retention. By applying the methodology based on grounded theory, this study allowed us to further contribute to the already available, yet limited, literature and definition of this multifaceted and greatly complex subject that is the phenomenon of churning.

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“…The work of [15], for instance, presents an approach using genetic algorithms to dynamically distribute human resources according to their age. The work of [16] proposes an approach using decision tree algorithms such as random forest, combining then with Support Vector Machine (SVM) and Naive Bayes as an approach to predict employees churn with data mining.…”

Section: A Bibliographic Review and Related Workmentioning

confidence: 99%

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

Goes

Oliveira

2020

IEEE Access

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This paper proposes a process for human resource performance evaluation using computational intelligence techniques. The human resource (or employee's) performance evaluation is essentially a regular assessment and review of an employee's performance on the job. This evaluation can be performed in different ways, depending on the kind of job of the employee and on the company's politics or business area. The process proposed on this research combines Fuzzy logic, text sentiment analysis and supervised learning classification techniques, such as a multi layer perceptron artificial neural network, decision tree algorithms and naïve bayes into ensemble classifiers, in an attempt to provide a fair evaluation process, minimizing or even eliminating common problems caused by simple objective or subjective approaches. The data provided for this research was originated from several evaluations applied in two Brazilians institutions. Simulation results shows consistence on the data generated by this proposed process, indicating a good perspective for applications on companies of most business areas.

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An approach for predicting employee churn by using data mining

Cited by 42 publications

References 10 publications

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

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