Predicting Employee Attrition Using Machine Learning Approaches

Raza, Ali; Munir, Kashif; Almutairi, Mubarak; Younas, Faizan; Fareed, Mian Muhammad Sadiq

doi:10.3390/app12136424

Cited by 41 publications

(25 citation statements)

References 31 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ETC is also an ensemble learning method widely used for the classification task [ 28 ]. The bagged decision trees are constructed in the ETC model for prediction.…”

Section: Study Methodologymentioning

confidence: 99%

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier

et al. 2023

Self Cite

View full text Add to dashboard Cite

Microbe organisms make up approximately 60% of the earth’s living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict microbe organisms in the human body. A novel hybrid microbes classifier (HMC) is proposed in this study which is based on a decision tree classifier and extra tree classifier using voting criteria. Experiments involve different machine learning and deep learning models for detecting ten different living microforms of life. Results suggest that the proposed HMC approach achieves a 98% accuracy score, 98% geometric mean score, 97% precision score, and 97% Cohen Kappa score. The proposed model outperforms employed models, as well as, existing state-of-the-art models. Moreover, the k-fold cross-validation corroborates the results as well. The research helps microbiologists identify the type of microbe organisms with high accuracy and prevents many diseases through early detection.

show abstract

“…ETC is also an ensemble learning method widely used for the classification task [ 28 ]. The bagged decision trees are constructed in the ETC model for prediction.…”

Section: Study Methodologymentioning

confidence: 99%

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Research done in [6] aims to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. IBM HR employee attrition dataset was used.…”

Section: Related Workmentioning

confidence: 99%

ETPSR: Employee Turnover Prediction with Security Recommendations

Chandgadkar,

Pathak,

Solanki

et al. 2024

Preprint

View full text Add to dashboard Cite

In today’s dynamic business environment, employee turnover poses a significantchallenge for organizations due to the competitive job market. Departing employ-ees may pose cybersecurity risks by retaining access to sensitive data and criticalsystems. This necessitates a focus on data security during the offboarding process.Traditional approaches lack proactive measures, relying on manual interventionsand potentially leaving gaps in data protection. To address this, the research pro-poses integrating a Cloud Services Security Recommendation (CSSR) model intothe prediction model. Leveraging machine learning and cybersecurity principles,the CSSR model identifies likely switchers and provides personalized security rec-ommendations. The research comprises of four components: a predictive model foridentifying potential switchers, an Explainable AI approach for behavior profilingand risk assessment, and integration with existing cybersecurity infrastructure toprovide tailored security recommendations. The CSSR model offers a proactivesolution to employee turnover and data security issues, enabling organizations tofortify their cybersecurity posture and protect sensitive information. The system employed in this study, the Employee Transition Prediction and Security Recom-mendations (ETPSR), integrates advanced analytics and cybersecurity principlesto offer tailored security measures during employee offboarding, ensuring dataintegrity and confidentiality throughout the transition process.

show abstract

“…The employee attrition rate is a crucial indicator providing valuable insights into the level of business development. The high attrition rate means that workers regularly quit their jobs ( Raza et al, 2022 ). However, the high rate can damage the company’s structure and result in management loss.…”

Section: Introductionmentioning

confidence: 99%

A transformer-based deep learning framework to predict employee attrition

2023

PeerJ Computer Science

View full text Add to dashboard Cite

In all areas of business, employee attrition has a detrimental impact on the accuracy of profit management. With modern advanced computing technology, it is possible to construct a model for predicting employee attrition to minimize business owners’ costs. Despite the reality that these types of models have never been evaluated under real-world conditions, several implementations were developed and applied to the IBM HR Employee Attrition dataset to evaluate how these models may be incorporated into a decision support system and their effect on strategic decisions. In this study, a Transformer-based neural network was implemented and was characterized by contextual embeddings adapting to tubular data as a computational technique for determining employee turnover. Experimental outcomes showed that this model had significantly improved prediction efficiency compared to other state-of-the-art models. In addition, this study pointed out that deep learning, in general, and Transformer-based networks, in particular, are promising for dealing with tabular and unbalanced data.

show abstract

Predicting Employee Attrition Using Machine Learning Approaches

Cited by 41 publications

References 31 publications

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier

ETPSR: Employee Turnover Prediction with Security Recommendations

A transformer-based deep learning framework to predict employee attrition

Contact Info

Product

Resources

About