Human resources (HR) of various groups (e.g., ethnic minority or majority) and their quality play a crucial role in developing and promoting economic and social progress in every region. However, current methods of quality assessment, e.g., surveys, have not provided data-driven insight for policymakers to design targeted interventions. Machine learning is one of the emerging technologies that could analyze complex datasets to support data insight for policymakers in sustainable economic development. This study proposes a framework to predict the quality of HR from ethnic minority community by using various machine learning techniques (K-nearest neighbors, multilayer perceptron, gradient boosting, and voting classifier). To achieve the best model, two techniques for feature selection (recursive feature elimination and extra trees) are employed. In the experiments, the ethnic minority HR data has been used to conduct. Experimental results show that the gradient boosting consistently outperformed other models across feature selection techniques (≥0.99). The findings from this study enhance prediction methods for HR and provide valuable insights for policymakers to develop effective policies for ethnic minority communities.

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.