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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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Updated: Sep 30, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Summary

Prioritizing co-beneficial Sustainable Development Goals (SDGs) is key to global progress. This study identifies specific influential SDGs based on country income and region, revealing unique, non-linear relationships for SDG achievement.

Keywords:
Co-beneficial SDGsData scienceGradient boosting machineMachine learningSDG prioritisationSustainable development goals

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Area of Science:

  • Global Development Studies
  • Data Science
  • Environmental Policy

Background:

  • The Sustainable Development Goals (SDGs) aim to tackle multifaceted global challenges across social, environmental, and economic domains.
  • Achieving all SDGs by 2030 is increasingly difficult due to their complexity, limited national budgets, and the impact of the COVID-19 pandemic.
  • Prioritizing co-beneficial goals offers an effective strategy to maximize impact on overall SDG achievement.

Purpose of the Study:

  • To identify the most influential Sustainable Development Goals (SDGs) that drive overall SDG achievement.
  • To explore the relationship between geographic location, income level, and SDG achievement.
  • To provide data-driven insights into the unique and non-linear interdependencies between SDGs.

Main Methods:

  • Application of the Gradient Boosting Machine (GBM) algorithm.
  • Data-driven analysis to identify key drivers of SDG scores.
  • Exploratory study examining regional and income-level variations in SDG influence.

Main Results:

  • Countries' geographic location and income level significantly correlate with overall SDG achievement.
  • The most influential SDGs vary by income level and region. Examples include SDG10 (high-income, Europe/Central Asia), SDG9 (upper-middle-income, Europe/Central Asia), SDG3 (low/lower-middle-income, Sub-Saharan Africa), and SDG5 (upper-middle-income, Latin America/Caribbean).
  • The study confirms unique and non-linear relationships between individual SDGs and overall SDG achievement.

Conclusions:

  • A targeted approach focusing on specific, co-beneficial SDGs is crucial for effective progress.
  • Understanding regional and income-specific SDG interdependencies is essential for tailored development strategies.
  • The findings highlight the complexity and non-uniformity of SDG achievement pathways globally.