Employee Turnover Prediction Using Machine Learning

Alaskar, Lama; Crane, Martin; Alduailij, Mai

doi:10.1007/978-3-030-36365-9_25

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…The Extreme Gradient Boosting (XGB) ( 27 ) model is an accurate and scalable version of boosting-tree models, with an aim to optimize the computing efficiency and predictive accuracy. XGB also uses regularization to control overfitting ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma

et al. 2022

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Hepatitis B Virus (HBV) infection may lead to various liver diseases such as cirrhosis, end-stage liver complications, and Hepatocellular carcinoma (HCC). Patients with existing cirrhosis or severe fibrosis have an increased chance of developing HCC. Consequently, lifetime observation is currently advised. This study gathered real-world electronic health record (EHR) data from the China Registry of Hepatitis B (CR-HepB) database. A collection of 396 patients with HBV infection at different stages were obtained, including 1) patients with a sustained virological response (SVR), 2) patients with HBV chronic infection and without further development, 3) patients with cirrhosis, and 4) patients with HCC. Each patient has been monitored periodically, yielding multiple visit records, each is described using forty blood biomarkers. These records can be utilized to train predictive models. Specifically, we develop three machine learning (ML)-based models for three learning tasks, including 1) an SVR risk model for HBV patients via a survival analysis model, 2) a risk model to encode the progression from HBV, cirrhosis and HCC using dimension reduction and clustering techniques, and 3) a classifier to detect HCC using the visit records with high accuracy (over 95%). Our study shows the potential of offering a comprehensive understanding of HBV progression via predictive analysis and identifies the most indicative blood biomarkers, which may serve as biomarkers that can be used for immunotherapy.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma

et al. 2022

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“…Sampe et al (2019) used LR and ANN via human resources data provided from Kaggle for the employee turnover prediction. Alaskar et al (2019) presented a comparison analysis of combination of five machine learning algorithms such as logistic regression, NB, DT, SVM, and AdaBoost and three feature selection methods such as selectkbest, recursive feature elimination (RFE), and RF for employee turnover prediction problem. In this study, experimental results showed that the two best models were DT with SelectKBest and the SVM–polynomial kernel using RF.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel deep learning model based on convolutional neural networks for employee churn prediction

Özmen

Özcan

2021

Journal of Forecasting

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Employees are one of the most important resources of a company. The churn of valuable employees significantly affects a company's performance. The design of systems that predict employee churn is critical importance for companies. At this point, machine learning algorithms offer important opportunities for the diagnosis of employee churn. Nowadays, traditional classification algorithms have been replaced by deep learning models. In this study, firstly, a convolutional neural network (CNN) model was applied on a numerical data set for employee churn prediction in retailing. Later, because the data loss is too much in data transformations, a new hybrid extended convolutional decision tree model (ECDT) was proposed by improving the CNN algorithm. Finally, a novel model (ECDT‐GRID) was developed by applying grid search optimization to improve the classification accuracy of ECDT. Numerical results showed that the developed ECDT‐GRID model outperformed the CNN and ECDT models and basic classification algorithms in terms of classification accuracy, and this model provided an efficient methodology for prediction of employee churn.

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“…As a scalable and accurate implementation of gradient boosted trees, optimized computational speed and model performance can be achieved. Reduction in the overfitting effect can be realized by XGB utilizing a regularization term compared to gradient boost, which is contributed to better prediction and much faster computational run times [63]. • The CatBoost Classifier [64] is a ML algorithm that uses gradient boosting on DTs.…”

Section: ) Model Trainingmentioning

confidence: 99%

A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset

et al. 2021

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Post-marketing surveillance of antineoplastic agents is performed to evaluate the efficacy and safety in patients aiming at expanding drug indications and discovering potential adverse events. The real-world data is fraught with missing values. Literature addressing different strategies for dealing with missing data in such a situation is scarce. Using machine learning (ML) algorithms for predicting therapeutic outcomes of PD-1/PD-L1 Inhibitors has attracted attention. However, training a predictive model usually requires imaging or biomarker information, which is rarely available in the post-marketing surveillance data. To address these challenges, we propose a ML-aided framework to predict the outcomes of Anti-PD-1 therapy for gynecological malignancy on a dataset with 117 patient samples, treated by Camrelizumab (with 50 patient samples), Sintilimab (44), and Toripalimab (23). Four therapeutic outcomes, including Response Evaluation Criteria in Solid Tumours (RECIST), organ adverse effect (AE), general AE, and death, are predicted. The proposed framework feeds the dataset into a learning pipeline consisting of imputation, feature engineering, model training, ensemble learning, and model selection to generate the final predictive model. We conduct experiments to justify several critical design choices, such as the specific feature engineering strategies and the SMOTE over-sampling technique. The final model for each learning task is selected from a large pool of model candidates based on a joint consideration of accuracy and F1. Moreover, we conduct thorough and visualized model analysis and gain a deeper understanding of model behavior and feature importance. The results, analysis, and findings demonstrate the superiority of the proposed learningaided framework.

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Employee Turnover Prediction Using Machine Learning

Cited by 16 publications

References 18 publications

Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma

Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma

A novel deep learning model based on convolutional neural networks for employee churn prediction

A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset

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