## 5 Discussion and Conclusions  
The primary aim of this study was to support HR managers in mitigating employee attrition by using predictive analytics to identify potential departures as early as possible. This predictive capability enables organizations to save valuable time and resources by reducing recruitment and training efforts. Additionally, it helps businesses meet deadlines and maintain stable staffing levels by preventing turnover. To accomplish this, we introduced a novel deep learning framework for employee attrition prediction based on the Bidirectional Temporal Convolutional Network (Bi-TCN). We utilized two publicly available datasets, IBM and Kaggle, for our experiments. The IBM dataset contained information from 1470 employees with 35 features, while the Kaggle dataset included 14,249 employees and 10 features. We conducted extensive experiments using both datasets, evaluating our model's performance against both cutting-edge methods and conventional machine learning and deep learning models in terms of important metrics including accuracy, precision, recall, F1-score, and AUC. The proposed model achieved an accuracy of 89.65% with five-fold cross-validation on the IBM dataset, and 97.83% on the Kaggle dataset. We also implemented a fully connected GAN-based data augmentation technique along with three oversampling methods including Random Oversampling, SMOTE, and ADASYN, to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Articles** & **Year** & **Model** & **Accuracy** \\ \hline [PERSON] (12) & 2019 & Decision Tree (DTJ48) & 82.44\% \\ [PERSON] (87) & 2020 & Logistic Regression (LR) & 87\% \\ [PERSON] et al. (88) & 2020 & SVM & 88.44\% \\  & & Linear SVC & 87.9\% \\ [PERSON] et al. (9) & 2020 & Gaussian NB & 82.5\% \\  & & Logistic Regression (LR) & 87.5\% \\  & & Logistic Regression (LR) & 88.43\% \\  & & DT + LR & 86.39\% \\ [PERSON] et al. (17) & 2021 & Random Forest (RF) & 87.298\% \\ [PERSON] et al. (23) & 2021 & LR without feature selection & 78.0\% \\ [PERSON] et al. (30) & 2021 & Deep Neural Network (DNN) & 89.11\% \\ [PERSON] (32) & 2021 & Neural Network & 84\% \\ [PERSON] et al. (90) & 2021 & Random Forest (RF) & 85.11\% \\ [PERSON] et al. (35) & 2022 & KNN & 84\% \\ [PERSON] et al. (26) & 2022 & RF & 80\% \\ [PERSON] et al. (10) & 2022 & CatBoost & 89.45\% \\ [PERSON] et al. (89) & 2023 & Extra Trees Classifier (10-fold) & 93\% \\  & & XG Boost & 86\% \\  & & SVM & 84\% \\ [PERSON] et al. (36) & 2024 & L-SVM & 87\% \\  & & L-SVM with features combination & 92.5\% \\  & & Graph Neural Network (GNN) & 70.4\% \\ [PERSON] et al. (41) & 2024 & Graph Attention Network (GAT) & 74.3\% \\ [PERSON] et al. (37) & 2024 & GA-DeepAutoencoder-KNN & 90.95\% \\ Proposed Model & 2025 & Ensemble Bi-TCN & 89.65\% \\ Proposed Model with Data & 2025 & Ensemble Bi-TCN+ (GAN) & 92.17\% \\ Augmentation & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparing the proposed model with earlier research on the IBM dataset. enhance and balance the IBM dataset. The results demonstrate that our proposed model, when combined with the GAN-based approach, achieves an accuracy of 92.17%. These results surpass most baseline machine learning and deep learning models, as well as state-of-the-art approaches, highlighting the model's potential to improve current methods and its broad applicability across various industries. Beyond merely putting algorithms into practice, our study analyzes the results in the particular context of HR decision-making, highlighting the significance of determining important aspects linked to employee attrition. We applied SHAP, an explainable AI method, to determine the most influential features contributing to attrition. By highlighting the crucial factors influencing attrition, the proposed model helps organizations create focused retention and recruitment plans in advance. The cost of recruiting and onboarding new staff can be significantly reduced with this calculated strategy.