설명 가능한 인공지능을 이용한 지역별 출산율 차이 요인 분석

• 영문명: Analysis of Regional Fertility Gap Factors Using Explainable Artificial Intelligence
• 발행기관: 한국산업경영시스템학회
• 저자명: 이동우(Dongwoo Lee) 김미경(Mi Kyung Kim) 윤정윤(Jungyoon Yoon) 류동원(Dongwon Ryu) 송재욱(Jae Wook Song)
• 간행물 정보: 『산업경영시스템학회지』제47권 제1호, 41~50쪽, 전체 10쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2024.03.30

영문 초록

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.

목차

1. 서 론

2. 선행연구

3. 연구 설계

4. 모형 및 학습 성능 비교

5. 설명 가능한 인공지능 기반 요인 분석

6. 결 론

Acknowledgement

References

키워드

Low Fertility Rate Explainable Artificial Intelligence Machine Learning Text Mining