Developing an intelligent decision support system for enhancing human resource management practices

The growing complexity and dynamic nature of Human Resource Management (HRM) necessitates the employment of sophisticated technological solutions to optimize decision-making and operational efficiency. HRM departments continue to struggle with optimizing recruitment processes, employee retention, and employee performance measurement. Conventional HRM practices are often based on subjective decision-making, resulting in inefficiencies, bias, and strategic workforce management opportunities. In response to these constraints, this research introduces a new Intelligent Decision Support System (IDSS) for HRM development. The system exploits the Resilient Nutcracker Mutated Scalable Random Forest (RN-SRF) machine learning (ML) approach to support improved decision-making through data-driven knowledge. The data is obtained from Kaggle, which includes public HR data with key features like employee demographics, performance scores, and job satisfaction levels. A data pre-processing workflow was adopted to maintain data quality and model effectiveness. The data was cleaned to handle missing values, remove outliers, and fix inconsistencies. Moreover, normalization was used to properly scale features to ensure the model learns efficiently without bias towards certain attributes. Python was used to implement and execute the RN-SRF model, and performance was evaluated by considering major metrics such as accuracy, recall, and F1-score. The accuracy of the model was 0.975, the recall was 99%, and the F1-score was 99%, which measures that the model is effective at prediction tasks. Most importantly, the model performed particularly well at predicting employee retention, accurately pinpointing at-risk employees and deriving actionable recommendations for enhancing recruitment and retention efforts. The performance measures asserted the stability and credibility of the RN-SRF model, providing HR practitioners with a sophisticated tool to refine workforce planning and maximize HR strategy.