Machine Learning-based Classification of Developing Countries and Exploration of Country-Specific ODA Strategies

Introduction: This study aimed to develop a systematic methodology for classifying recipient countries using machine learning, with the premise that tailoring mid- to long-term ODA strategies to country characteristics is essential. Additionally, it sought to propose ODA policy directions considering the unique attributes of classified developing countries. Methods: The research analyzed 166 countries, including both developed and developing nations, using SDG scores and GDP per capita as key indicators. Machine learning techniques, specifically neural network analysis and decision tree analysis, were employed for classification. Results: The analysis resulted in the classification of the 166 countries into 12 distinct groups, with seven nodes representing developing countries. Each group exhibited unique characteristics that informed the development of country-specific ODA strategies Conclusions: This study successfully developed a systematic classification methodology for recipient countries using machine learning. The resulting classification and proposed ODA strategies for each group provide a foundation for more targeted and effective ODA policies. This approach enables policymakers to tailor their strategies to the specific needs and characteristics of different developing country groups, potentially improving the impact and efficiency of ODA efforts.