An app for predicting nurse intention to quit the job using artificial neural networks (ANNs) in Microsoft Excel

Abstract

Background: Numerous studies have identified factors related to nurses' intention to leave. However, none has successfully predicted the nurse's intention to quit the job. Whether the intention to quit the job can be predicted is an interesting topic in healthcare settings. A model to predict the nurse's intention to quit the job for novice nurses should be investigated. The aim of this study is to build a model to develop an app for the automatic prediction and classification of nurses' intention to quit their jobs.Methods: We recruited 1104 novice nurses working in 6 medical centers in Taiwan to complete 100-item questionnaires related to the nurse's intention to quit the job in October 2018. The k-mean was used to divide nurses into 2 classes based on 5 items regarding leave intention. Feature variables were selected from the 100-item survey. Two models, including an artificial neural network (ANN) and a convolutional neural network, were compared across 4 scenarios made up of 2 training sets (n = 1104 and n = 804 ≅ 70%) and their corresponding testing (n = 300 ≅ 30%) sets to verify the model accuracy. An app for predicting the nurse's intention to quit the job was then developed as a website assessment.Results: We observed that 24 feature variables extracted from this study in the ANN model yielded a higher area under the ROC curve of 0.82 (95% CI 0.80-0.84) based on the 1104 cases, the ANN performed better than the convolutional neural network on the accuracy, and a ready and available app for predicting the nurse's intention to quit the job was successfully developed in this study.Conclusions: A 24-item ANN model with 53 parameters estimated by the ANN was developed to improve the accuracy of nurses' intention to quit their jobs. The app would help team leaders take care of nurses who intend to quit the job before their actions are taken.Abbreviations: ANN = artificial neural network, AUC = area under the ROC curve, CNN = convolutional neural network.

Classification and citation analysis of the 100 top-cited articles on nurse resilience using chord diagrams: A bibliometric analysis

“…[14] Resilient individuals can recover from difficult situations and lower their levels of burnout, thereby reducing turnover intentions. [15][16][17][18] As a result of multiple sources of support and improvements in personal accomplishment, resilience is increased and burnout is reduced, resulting in a decrease in turnover intentions. [18] Both internal and external factors will influence turnover intentions during the transition from nursing students to registered nurses.…”

“…[15][16][17][18] As a result of multiple sources of support and improvements in personal accomplishment, resilience is increased and burnout is reduced, resulting in a decrease in turnover intentions. [18] Both internal and external factors will influence turnover intentions during the transition from nursing students to registered nurses. It is, therefore, necessary to conduct a broad bibliometric analysis to explore the knowledge and understanding of details related to nurser resilience for newcomers in the nursing profession.…”

“…[15–18] As a result of multiple sources of support and improvements in personal accomplishment, resilience is increased and burnout is reduced, resulting in a decrease in turnover intentions. [18]…”

An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel

“…Microsoft Excel is a familiar program to many researchers. We are motivated to compare the accuracy of 2 popular ML models (i.e., convolutional neural networks, [CNN] and artificial neural networks [ANN]) that have been demonstrated in the literature, [25][26][27][28][29][30][31][32] but with binary classes only. In general, CNN is considered to be a more powerful and accurate method of solving classification problems.…”

“…The 2 popular ML models (i.e., CNN and ANN) have also been demonstrated separately in MS Excel. [25][26][27][28][29][30][31][32] Many researchers are familiar with the use of Microsoft Excel. Until now, no research has been conducted comparing model accuracy between the 2 ML models under the MS Excel environment, particularly when the modules and Equations are detailed in Supplemental Digital Contents, as we did in this research.…”