Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System

Tu, Xiang; Wu, Ping Zhen; Yuan, Shihai

doi:10.1155/2022/7425815

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Loyarte-López and García-Olaizola (2022) present an ML-based method for evaluating the internal value of talent and ensuring internal equity in salary criteria, suggesting that ML can support equitable and unbiased salary decisions based on data. Xiang et al (2022) propose an intelligent HRM system that combines backpropagation neural network and logistic regression analysis to improve effectiveness, which is verified through simulation tests with good practical effects. Vrontis et al (2022) analyzed 45 HRM field journals using intelligent automation, including artificial intelligence, and explained that automation technologies present a new approach (e.g., technical and ethical level) in the HRM research field.…”

Section: Literature Reviewmentioning

confidence: 91%

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

Lee,

Lim

et al. 2024

Administrative Sciences

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An organization’s human resource management practices are essential for its competitive advantage. This study specifically examined human resource (HR) practices that predict corporate performance (employee turnover and firm sales) based on a backpropagation neural network (BPN)-based causality analysis. This study aims to test how to optimize human resource practices to improve organizational performance. This study elucidated the effect of HR practices and organizational-level factors on predicting employee turnover and firm sales. The BPN-based causality analysis revealed the relative importance of explanatory variables on firm performance. To test the model, it employed the Human Capital Corporate Panel open data on Korean companies’ HR practices and other characteristics. The analysis identifies causal relationships between specific HR practices and firm performance. The results show that compensation-related HR practices are most influential in predicting firm sales and employee turnover. Moreover, training-related HR practices were modest, and talent acquisition and performance management practices had relatively weak effects on the two outcomes. The study provides insights into how human resource practices can be optimized to improve firm performance and enhance organizational effectiveness. The findings of this study contribute to the growing body of research on the use of machine learning in HR management and suggest practical implications for managers’ insights to optimize HR practices.

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Section: Literature Reviewmentioning

confidence: 91%

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

Lee,

Lim

et al. 2024

Administrative Sciences

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“…According to studies, logistic regression does not demonstrate a worse classification ability than other machine learning methods ( Christodoulou et al, 2019 ; Song et al, 2021 ). Logistic regression analysis can be used to estimate the probability of a certain output class based on some input variables ( Meurer and Tolles, 2017 ; Xiang et al, 2022 ). Decision tree is a classical machine learning algorithm that can be used for classification and regression problems.…”

Section: Methodsmentioning

confidence: 99%

A lung cancer risk warning model based on tongue images

et al. 2023

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Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods.Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets.Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively.Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.

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“…One result of the dichotomous response variable Y is denoted as “success,” denoted by 1. The other result is denoted as “failure,” denoted by 0 [ 16 ]. Its general form is as follows:

…”

Section: Methodsmentioning

confidence: 99%

Construction and Evaluation of Prognosis Prediction Model for Patients with Brain Contusion and Laceration Based on Machine Learning

Bai

Zheng

2022

Computational and Mathematical Methods in Medicine

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Objective. Finding valuable risk factors for the prognosis of brain contusion and laceration can help patients understand the condition and improve the prognosis. This study is aimed at analyzing the risk factors of poor prognosis in patients with brain contusion after the operation. Methods. A total of 136 patients with cerebral contusion and laceration combined with cerebral hernia treated by neurosurgical craniotomy in our hospital were retrospectively selected and divided into a training set ( n = 95 ) and a test set ( n = 41 ) by the 10-fold crossover method. Logistic regression and back-propagation neural network prediction models were established to predict poor prognosis factors. The receiver operating characteristic curve (ROC) and the calibration curve were used to verify the differentiation and consistency of the prediction model. Results. Based on logistic regression and back-propagation neural network prediction models, GCS score ≤ 8 on admission, blood loss ≥ 30 ml, mannitol ≥ 2 weeks, anticoagulants before admission, and surgical treatment are the risk factors that affect the poor prognosis of patients with a cerebral contusion after the operation. The area under the ROC was 0.816 (95% CI 0.705~0.926) and 0.819 (95% CI 0.708~0.931), respectively. Conclusion. The prediction model based on the risk factors that affect the poor prognosis of patients with brain contusion and laceration has good discrimination and accuracy.

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Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System

Cited by 5 publications

References 17 publications

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

A lung cancer risk warning model based on tongue images

Construction and Evaluation of Prognosis Prediction Model for Patients with Brain Contusion and Laceration Based on Machine Learning

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