标题： 一种预测海湾合作委员会国家人类发展指数（HDI）趋势的数值评估 显示英文标题

标题： A Numerical Assessment for Predicting Human Development Index (HDI) Trends in the GCC Countries

摘要： 本研究专注于使用机器学习技术，特别是XGBoost算法，预测海湾合作委员会（GCC）国家沙特阿拉伯、卡塔尔、科威特、巴林、阿联酋和阿曼的人类发展指数（HDI）趋势。HDI是预期寿命、教育和收入的综合指标，反映了整体人类发展状况。数据从官方政府来源和国际数据库（包括世界银行和联合国开发计划署）收集，涵盖1996年至2022年期间。使用实数序列上的编辑距离（EDR）方法进行特征选择，该模型分析关键指标以预测未来五年（2023-2027年）的HDI值。该模型在样本内数据中表现出强大的预测准确性，但在样本外预测中出现了轻微的过拟合问题，特别是在阿联酋的情况下。预测结果表明，科威特、巴林和阿联酋的人类发展指数值将保持稳定或略有上升，而沙特阿拉伯、卡塔尔和阿曼可能会出现最小波动或小幅下降。本研究强调了经济、健康和教育指标在决定HDI趋势中的重要性，并强调了需要区域特定的预测模型以提高准确性。政策制定者应重点关注医疗保健、教育和经济多元化方面的针对性干预措施，以改善人类发展成果。 显示英文摘要

摘要： This study focuses on predicting the Human Development Index (HDI) trends for GCC countries Saudi Arabia, Qatar, Kuwait, Bahrain, United Arab Emirates, and Omanusing machine learning techniques, specifically the XGBoost algorithm. HDI is a composite measure of life expectancy, education, and income, reflecting overall human development. Data was gathered from official government sources and international databases, including the World Bank and UNDP, covering the period from 1996 to 2022. Using the Edit Distance on Real sequence (EDR) method for feature selection, the model analyzed key indicators to predict HDI values over the next five years (2023-2027). The model demonstrated strong predictive accuracy for in-sample data, but minor overfitting issues were observed with out-of-sample predictions, particularly in the case of the UAE. The forecast results suggest that Kuwait, Bahrain, and the UAE will see stable or slightly increasing HDI values, while Saudi Arabia, Qatar, and Oman are likely to experience minimal fluctuations or slight decreases. This study highlights the importance of economic, health, and educational indicators in determining HDI trends and emphasizes the need for region-specific predictive models to improve accuracy. Policymakers should focus on targeted interventions in healthcare, education, and economic diversification to enhance human development outcomes.