Xu, Y.; Park, Y.; Park, J.D.; Sun, B. Predicting Nurse Turnover for Highly Imbalanced Data Using the Synthetic Minority over-Sampling Technique and Machine Learning Algorithms. Healthcare2023, 11, 3173.
Xu, Y.; Park, Y.; Park, J.D.; Sun, B. Predicting Nurse Turnover for Highly Imbalanced Data Using the Synthetic Minority over-Sampling Technique and Machine Learning Algorithms. Healthcare 2023, 11, 3173.
Xu, Y.; Park, Y.; Park, J.D.; Sun, B. Predicting Nurse Turnover for Highly Imbalanced Data Using the Synthetic Minority over-Sampling Technique and Machine Learning Algorithms. Healthcare2023, 11, 3173.
Xu, Y.; Park, Y.; Park, J.D.; Sun, B. Predicting Nurse Turnover for Highly Imbalanced Data Using the Synthetic Minority over-Sampling Technique and Machine Learning Algorithms. Healthcare 2023, 11, 3173.
Abstract
Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.
Keywords
Nurse Turnover; Machine Learning; SMOTE; NSSRN; Random Forest; XGoost
Subject
Public Health and Healthcare, Nursing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
