scholarly journals Determinates of Employee Voluntary Turnover and Forecasting in R&D Departments: A Case Study

2016 ◽  
Vol 3 (1) ◽  
pp. 64
Author(s):  
Xiaojuan Zhu ◽  
Rapinder Sawhney ◽  
Girish Upreti
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Human Resource ◽  
Voluntary Turnover ◽  
Important Variable ◽  
Validation Dataset ◽  
Tree Model ◽  
Job Title ◽  
Decision Tree Method ◽  
Employee Tenure

employee voluntary turnover factors using logistic regression and forecasts employee tenure using a decision tree for four research and development departments in a large U.S organization. Company job title, gender, ethnicity, age and years of service significantly affect employee voluntary turnover behavior determined by logistic regression. The findings assist managers and human resource departments in specific employee retention strategies to reduce R&D departments’ voluntary turnover rate. The decision tree method built a five-level depth tree model with 17 nodes. This model has the lowest AIC value and the best performance in the validation dataset. Age at hire, jobtitle, division, and race are statistically significant factors to predict employee tenure. The most important variable is age at hire located in the decision tree’s first, third, and fourth nodes. Classification rules assist managers and human resource departments in quickly predicting employee tenure and in making hiring decisions.

scholarly journals Predicting voluntary turnover through human resources database analysis

2018 ◽  
Vol 41 (1) ◽  
pp. 96-112 ◽  
Author(s):  
Evy Rombaut ◽  
Marie-Anne Guerry
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Human Resources ◽  
Real Life ◽  
Model Performance ◽  
Voluntary Turnover ◽  
Private Company ◽  
Data Set ◽  
Content Type ◽  
Individual Level

Purpose This paper aims to question whether the available data in the human resources (HR) system could result in reliable turnover predictions without supplementary survey information. Design/methodology/approach A decision tree approach and a logistic regression model for analysing turnover were introduced. The methodology is illustrated on a real-life data set of a Belgian branch of a private company. The model performance is evaluated by the area under the ROC curve (AUC) measure. Findings It was concluded that data in the personnel system indeed lead to valuable predictions of turnover. Practical implications The presented approach brings determinants of voluntary turnover to the surface. The results yield useful information for HR departments. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk. Originality/value The study of a data-driven approach for turnover investigation has not been done so far.

scholarly journals Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Ru Zhu ◽  
Hua Duan ◽  
Sha Wang ◽  
Lu Gan ◽  
Qian Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Endometrial Thickness ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Decision Tree Model ◽  
Classification And Regression Tree ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Tree Model

Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients’ demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions. The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions.

scholarly journals Common Metamodel of Questionnaire Model and Decision Tree Model

2018 ◽  
Vol 10 (3) ◽  
pp. 106
Author(s):  
Mirza Suljic ◽  
Edin Osmanbegovic ◽  
Željko Dobrović
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Decision Tree Model ◽  
Tree Model ◽  
Meta Model ◽  
The Subject ◽  
Decision Tree Method ◽  
Questionnaire Method ◽  
Tree Method

The subject of this paper is metamodeling and its application in the field of scientific research. The main goal is to explore the possibilities of integration of two methods: questionnaires and decision trees. The questionnaire method was established as one of the methods for data collecting, while the decision tree method represents an alternative way of presenting and analyzing decision making situations. These two methods are not completely independent, but on the contrary, there is a strong natural bond between them. Therefore, the result reveals a common meta-model that over common concepts and with the use of metamodeling connects the methods: questionnaires and decision trees. The obtained results can be used to create a CASE tool or create repository that can be suitable for exchange between different systems. The proposed meta-model is not necessarily the final product. It could be further developed by adding more entities that will keep some other data.

scholarly journals Prediction of health care expenditure increase: how does pharmacotherapy contribute?

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Annika M. Jödicke ◽  
Urs Zellweger ◽  
Ivan T. Tomka ◽  
Thomas Neuer ◽  
Ivanka Curkovic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Decision Tree ◽  
Health Care Costs ◽  
Decision Tree Model ◽  
Tree Model ◽  
Physician Visits ◽  
Drug Classes ◽  
Swiss Health ◽  
Care Costs

Abstract Background Rising health care costs are a major public health issue. Thus, accurately predicting future costs and understanding which factors contribute to increases in health care expenditures are important. The objective of this project was to predict patients healthcare costs development in the subsequent year and to identify factors contributing to this prediction, with a particular focus on the role of pharmacotherapy. Methods We used 2014–2015 Swiss health insurance claims data on 373′264 adult patients to classify individuals’ changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks. Based on the decision tree model, we performed a detailed feature importance analysis and subgroup analysis, with an emphasis on drug classes. Results The boosted decision tree model achieved an overall accuracy of 67.6% and an area under the curve-score of 0.74; the neural network and logistic regression models performed 0.4 and 1.9% worse, respectively. Feature engineering played a key role in capturing temporal patterns in the data. The number of features was reduced from 747 to 36 with only a 0.5% loss in the accuracy. In addition to hospitalisation and outpatient physician visits, 6 drug classes and the mode of drug administration were among the most important features. Patient subgroups with a high probability of increase (up to 88%) and decrease (up to 92%) were identified. Conclusions Pharmacotherapy provides important information for predicting cost increases in the total population. Moreover, its relative importance increases in combination with other features, including health care utilisation.

Application Research on Traffic Modal Choice Based on Decision Tree Algorithm

2011 ◽  
Vol 97-98 ◽  
pp. 843-848
Author(s):  
Zheng Hong Peng ◽  
Xin Luan
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Tree Model ◽  
Modal Choice ◽  
Sample Data ◽  
Urbanization In China ◽  
Decision Tree Method ◽  
Information Gain Ratio

With the rapid development of urbanization in china, the contradiction between transport, environment and population growth is becoming more and more pronounced, which offers higher demands for transport planning. This article mainly describes the application of decision tree learning algorithm in traffic modal choice. First preprocess the sample data, then calculate and analyze the information gain ratio, and finally we will build a decision tree model. The results show that the rules obtained by decision tree method have some practical value in the analysis of traffic modal choice.

scholarly journals Prediction of Frost Occurrences Using Statistical Modeling Approaches

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hyojin Lee ◽  
Jong A. Chun ◽  
Hyun-Hee Han ◽  
Sung Kim
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Prediction Models ◽  
Warning System ◽  
Probability Of Detection ◽  
Tree Model ◽  
Explanatory Variables ◽  
Topological Features ◽  
Operational Activities ◽  
Timely Warning

We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.

scholarly journals ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

2018 ◽  
Vol 2 (2) ◽  
pp. 178
Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Training Data ◽  
Prediction System ◽  
Tree Model ◽  
Average Accuracy ◽  
C4.5 Algorithm ◽  
Academic Grades ◽  
Decision Tree Method ◽  
Tree Method

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

scholarly journals Application of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted magnetic resonance imaging in the differential diagnosis of metastases and myeloma in the spine-Univariate and multivariate analysis of related parameters

2020 ◽  
Vol 93 (1112) ◽  
pp. 20190891
Author(s):  
Xiaoying Xing ◽  
Jiahui Zhang ◽  
Yongye Chen ◽  
Qiang Zhao ◽  
Ning Lang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Diffusion Coefficient ◽  
Decision Tree ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Decision Tree Model ◽  
Tree Model ◽  
Stretched Exponential ◽  
Exponential Models

Objective: To explore the value of related parameters in monoexponential, biexponential, and stretched-exponential models of diffusion-weighted imaging (DWI) in differentiating metastases and myeloma in the spine. Methods: 53 metastases and 16 myeloma patients underwent MRI with 10 b-values (0–1500 s/mm2). Parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), the distribution diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) from DWI were calculated. The independent sample t test and the Mann–Whiney U test were used to compare the statistical difference of the parameter values between the two. Receiver operating characteristics (ROC) curve analysis was used to identify the diagnostic efficacy. Then substituted each parameter into the decision tree model and logistic regression model, identified meaningful parameters, and evaluated their joint diagnostic performance. Results: The ADC, D, and α values of metastases were higher than those of myeloma, whereas the D* value was lower than that of myeloma, and the difference was significant (p < 0.05); the area under the ROC curve for the above parameters was 0.661, 0.710, 0.781, and 0.743, respectively. There was no significant difference in the f and DDC values (p > 0.05). D and α were found to conform to the decision tree model, and the accuracy of model diagnosis was 84.1%. ADC and α were found to conform to the logistic regression model, and the accuracy was 87.0%. Conclusion: The 3 models of DWI have certain values indifferentiating metastases and myeloma in spine, and the diagnostic performance of ADC, D, α and D*was better. Combining ADC with α may markedly aid in the differential diagnosis of the two. Advances in knowledge: Monoexponential, biexponential, and stretched-exponential models can offer additional information in the differential diagnosis of metastases and myeloma in the spine. Decision tree model and logistic regression model are effective methods to help further distinguish the two.

scholarly journals Research on Driver Behavior in Yellow Interval at Signalized Intersections

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Xiujuan Tian ◽  
Wei Wang ◽  
Xiyang Zhou ◽  
Hongmei Liang
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Driver Behavior ◽  
Signalized Intersections ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Model ◽  
Dilemma Zone

Vehicles are often caught in dilemma zone when they approach signalized intersections in yellow interval. The existence of dilemma zone which is significantly influenced by driver behavior seriously affects the efficiency and safety of intersections. This paper proposes the driver behavior models in yellow interval by logistic regression and fuzzy decision tree modeling, respectively, based on camera image data. Vehicle’s speed and distance to stop line are considered in logistic regression model, which also brings in a dummy variable to describe installation of countdown timer display. Fuzzy decision tree model is generated by FID3 algorithm whose heuristic information is fuzzy information entropy based on membership functions. This paper concludes that fuzzy decision tree is more accurate to describe driver behavior at signalized intersection than logistic regression model.

Sign in / Sign up

Export Citation Format

Share Document