## 2 Related Works
The framework's efficacy in predicting employee departure based on job attitudes and other internal and external factors was demonstrated through a comparison of various performance metrics. Several researchers have utilized Logistic Regression (LR) for predicting employee turnover [5; 22]. [PERSON] et al. [23] proposed a three-stage approach for attrition prediction, encompassing pre-processing, processing, and post-processing phases. In the pre-processing phase, the authors introduced the m-max-out method for feature selection. Logistic Regression (LR) was then employed as the classification technique. The model's validity was assessed by examining the changes in its parameters across multiple bootstrap datasets, verifying the robustness of the LR model for attrition prediction. These pre-processing and post-processing stages contribute to the development of more accurate and reliable models for employee turnover forecasting. Several studies have demonstrated that boosting algorithms tend to outperform other machine learning techniques when it comes to forecasting employee turnover [24; 25]. [PERSON] et al. [26] employed an enhanced feature engineering approach in conjunction with the boosting algorithm CatBoost to predict and analyze employee turnover usingthe IBM dataset. According to experimental results, the proposed method outperformed existing models. Specifically, it achieved an accuracy of 89.45% and an F1-score of 88.0% for employee attrition prediction on the IBM dataset. In another study, [PERSON] et al. [27] proposed an approach based on multi-attribute decision-making (MADM) and machine learning algorithms, referred to as \"Employee Classification and Prediction for Retention\" (ECPR). Using a two-stage MADM technique, they developed an accomplishment-based employee importance model (AEIM) to divide staff into different groups. To assign relative weights to employee accomplishments, they introduced an enhanced version of the entropy weight method (IEWM). Employee performance significance within each class was then measured using the order preference by similarity to ideal solution (TOPSIS) approach. Subsequently, employee turnover was predicted for each class using the CatBoost algorithm. Finally, a retention strategy was proposed based on the feature ratings and the predictive findings. Several scholars have focused on utilizing deep learning and neural network models to predict employee turnover [28; 29]. [PERSON] et al. [30] employed a Deep Neural Network (DNN) combined with several pre-processing techniques to improve employee attrition prediction. Their model obtained an accuracy of 89.11% using 10-fold cross-validation on the original IBM dataset. To enhance the realism of the results, they also created a balanced version of the dataset, which led to an accuracy increase to 94%. In a separate study [31], deep learning approaches were explored to enhance the accuracy of employee turnover prediction. The authors proposed a multi-layered neural network architecture that integrates data from diverse sources, including demographics, employee engagement metrics, and historical turnover data. By combining feedforward and recurrent networks, the model effectively captures complex relationships and temporal dependencies in the data. Furthermore, advanced feature engineering techniques were employed to transform raw data into valuable inputs, significantly boosting the model's predictive performance. The experimental results indicated that the proposed approach outperformed traditional machine learning methods in terms of accuracy and reliability. Furthermore, [PERSON] [32] developed a novel data mining model that incorporates Information Gain and Chi-Square as feature selection techniques. These methods were used to identify the four most significant features in the dataset: overtime, job level, salary, and years in the organization. Several classification algorithms, including Decision Tree, SVM, Random Forest, Neural Network, and Naive Bayes, were applied to construct the model.