Integrating generative AI and machine learning classifiers for solving heterogenous MCGDM: a case of employee churn prediction.

Employee churn is a critical issue for companies and organizations, as it directly impacts productivity, efficiency, and overall operational success. High turnover rates increase recruitment and training costs, and disrupt workflows, making it a top concern for institutions aiming to maintain stability, growth and continuity. This study presents a methodology to address the employee churn prediction problem in heterogeneous environments by framing it as a Multiple Criteria Group Decision Making (MCGDM) problem. The proposed methodology integrates generative AI, Traditional MCGDM techniques, and machine learning classifiers to handle this problem type. The proposed methodology is structured into four main stages: data collection, generative AI for creating expert profiles, MCGDM for employee ranking, and machine learning for predictive modeling. ChatGPT-4 is used as the generative AI model to simulate expert profiles from diverse fields related to churn prediction. The Analytical Hierarchy Process (AHP) is employed to calculate criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is concerned with alternatives' ranking and employee's classification into churn likelihood categories. These rankings data are then used for different nine machine learning classifiers, reducing the computational complexity for future predictions. The results reveal that Neural Networks, Gradient Boosting, and Random Forest outperform other used models in predicting employee churn in terms of accuracy. The proposed methodology offers a scalable, data-driven solution for addressing MCGDM problems, particularly employee churn prediction, by integrating advanced AI techniques with traditional decision-making frameworks.