## 2 Related Works
Based on the implementation results on the IBM dataset, the Neural Network approach emerged as the most successful, obtaining an accuracy of 84%. Additionally, the authors of [33] proposed an event-centered turnover prediction method called CoxRF, which integrates ensemble learning with the statistical insights of survival analysis. They completely used restricted data by introducing the ideas of \"event-person\" and \"time-event\" to generate survival statistics. Their findings highlight several key insights: (i) Gender is a significant factor influencing staff attrition behavior, with the female staff exhibiting a higher attrition rate than their male counterparts; (ii) external factors, like the growth of Gross domestic product (GDP), have a notable impact on employee turnover, a consideration that has been largely overlooked in most studies; (iii) Different industries have different trends in staff attrition; the IT industry has a far greater rate than the government sector; (iv) Strongly educated staff typically leave their jobs more frequently than those with less education, especially after three to five years. [PERSON] et al. [34] proposed a turnover prediction technique called RFRSF, which develops a hybrid model that integrates machine learning with survival analysis. This approach combines ensemble learning for predicting turnover behavior with survival analysis to handle censored data. In order to create survival statistics using restricted information, the authors also implement methods to address employees with multiple turnover records. Using a real dataset taken from one of China's biggest professional social media platforms, they compare the performance of RFRSF with several baseline techniques. The findings demonstrate that the survival analysis component greatly enhances the effectiveness of employee attrition prediction. The Random Forest (RF) and K-Nearest Neighbors (KNNs) methods served as the foundation for the models put out by [35]. According to the experimental results, the KNN-based method outperformed the RF-based method, achieving an accuracy of 84.0% compared to 80.0% on the IBM dataset. [PERSON] et al. [36] introduced a unique method that utilizes Graph Convolutional Networks (GCNs) to convert typical tabular employee data into a knowledge graph structure in order to extract more subtle information. The method integrates both graph-derived information and the original IBM dataset to predict employee turnover. Several machine learning models were employed to evaluate classification performance across various criteria, with the results indicating that the Linear Support Vector Machine (L-SVM) emerged as the most effective model. A hybrid model combining an Autoencoder, a Genetic Algorithm, and K-Nearest Neighbor was proposed by [37] for forecasting employee turnover, named GA-DeepAutoencoder-KNN. The approach enhances prediction accuracy by integrating the KNN model, an Autoencoder, and a Genetic Algorithm. The model was empirically assessed and contrasted with standard KNN and DeepAutoencoder-KNN methods. The results demonstrated that, using the IBM dataset, the GA-DeepAutoencoder-KNN method obtained an accuracy of 90.95%, outperforming the DeepAutoencoder-KNN model (86.48%) and the KNN model (88.37%). A few studies also focus on explainable AI (XAI) methods to identify the most influential features and provide valuable insights for HR decision-making. [PERSON] et al. [38] explored how explainable AI (XAI) can be used to detect possible staff attrition and develop data-driven solutions to deal with this challenging issue. Initially, they concentrated on using machine learning models for predicting employee attrition. Then, in order to improve the transparency and interpretability of AI models, they used explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanation) [39], and LIME (Local Interpretable Model-agnostic Explanations) [40].