Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Xu, Yuan; Park, Yongshin; Park, Ju dong; Sun, Bora

doi:10.20944/preprints202311.0049.v1

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…This prediction was based on eighteen carefully selected demographic and work-related categorical variables, utilizing the logit, Random Forest (RF), Decision Tree (DT), and XGBoost algorithms. Notably, the RF model exhibited superior performance 90 .…”

Section: Similar Workmentioning

confidence: 99%

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Gadár,

Abonyi

2024

Sci Rep

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In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.

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Section: Similar Workmentioning

confidence: 99%

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Gadár,

Abonyi

2024

Sci Rep

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Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

Won,

Lim,

Kang

2024

Applied Sciences

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This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems.

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The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning

Holgado-Apaza,

Ulloa-Gallardo,

Aragon-Navarrete

et al. 2024

Sustainability

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Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.

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Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Cited by 3 publications

References 31 publications

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning

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