Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

1. 서 론
    1.1 연구의 배경 및 목적
2. 선행연구
    2.1 선행연구 고찰
    2.2 연구 방향성 탐색
3. 연구 설계
    3.1 데이터 설정
    3.2 텍스트 마이닝을 통한 파생변수 생성
4. 모형 및 학습 성능 비교
    4.1 기계 학습 방법론
    4.2 모형 학습 성능
5. 설명 가능한 인공지능 기반 요인 분석
    5.1 Shapley Additive exPlanations
    5.2 종합 요인 분석
    5.3 지역별 요인 분석
6. 결 론
References

• Dongwoo Lee(Department of Industrial Engineering, Hanyang University) | 이동우 (한양대학교 산업공학과)
• Mi Kyung Kim(Department of Industrial Engineering, Hanyang University) | 김미경 (한양대학교 산업공학과)
• Jungyoon Yoon(Department of Industrial Engineering, Hanyang University) | 윤정윤 (한양대학교 산업공학과)
• Dongwon Ryu(Department of Industrial Engineering, Hanyang University) | 류동원 (한양대학교 산업공학과)
• Jae Wook Song(Department of Industrial Engineering, Hanyang University) | 송재욱 (한양대학교 산업공학과) Corresponding author