Employee Attrition Prediction Using Deep Neural Networks
Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artifici...
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MDPI AG
2021
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oai:doaj.org-article:df25ac9a57f94de5a5c028922f2eeafd2021-11-25T17:17:24ZEmployee Attrition Prediction Using Deep Neural Networks10.3390/computers101101412073-431Xhttps://doaj.org/article/df25ac9a57f94de5a5c028922f2eeafd2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/141https://doaj.org/toc/2073-431XDecision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.Salah Al-DarrajiDhafer G. HoniFrancesca FallucchiAyad I. AbdulsadaRomeo GiulianoHusam A. AbdulmalikMDPI AGarticledeep learningmachine learningattrition predictionElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 141, p 141 (2021) |
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deep learning machine learning attrition prediction Electronic computers. Computer science QA75.5-76.95 |
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deep learning machine learning attrition prediction Electronic computers. Computer science QA75.5-76.95 Salah Al-Darraji Dhafer G. Honi Francesca Fallucchi Ayad I. Abdulsada Romeo Giuliano Husam A. Abdulmalik Employee Attrition Prediction Using Deep Neural Networks |
description |
Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset. |
format |
article |
author |
Salah Al-Darraji Dhafer G. Honi Francesca Fallucchi Ayad I. Abdulsada Romeo Giuliano Husam A. Abdulmalik |
author_facet |
Salah Al-Darraji Dhafer G. Honi Francesca Fallucchi Ayad I. Abdulsada Romeo Giuliano Husam A. Abdulmalik |
author_sort |
Salah Al-Darraji |
title |
Employee Attrition Prediction Using Deep Neural Networks |
title_short |
Employee Attrition Prediction Using Deep Neural Networks |
title_full |
Employee Attrition Prediction Using Deep Neural Networks |
title_fullStr |
Employee Attrition Prediction Using Deep Neural Networks |
title_full_unstemmed |
Employee Attrition Prediction Using Deep Neural Networks |
title_sort |
employee attrition prediction using deep neural networks |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/df25ac9a57f94de5a5c028922f2eeafd |
work_keys_str_mv |
AT salahaldarraji employeeattritionpredictionusingdeepneuralnetworks AT dhaferghoni employeeattritionpredictionusingdeepneuralnetworks AT francescafallucchi employeeattritionpredictionusingdeepneuralnetworks AT ayadiabdulsada employeeattritionpredictionusingdeepneuralnetworks AT romeogiuliano employeeattritionpredictionusingdeepneuralnetworks AT husamaabdulmalik employeeattritionpredictionusingdeepneuralnetworks |
_version_ |
1718412563747700736 |