RecomMomd: AI-based Maternity Hospital Matching System in Chungbuk

🏥 RecomMomd: AI-based Maternity Hospital Matching System in Chungbuk

Team Project | Jun 2025

Tackled the low birth rate problem and lack of maternity hospital accessibility in Chungbuk Province.
Built a hospital recommendation pipeline integrating structured healthcare datasets (delivery success rates, C-section ratios, NICU availability) with unstructured review data (Naver blogs, maps).
Implemented KoBERT-based sentiment analysis to classify hospital reviews and highlight features valued by expectant mothers.
Applied TF-IDF and LDA to extract keywords and discover latent themes in hospital choice motivations.
Automated data collection via Selenium + BeautifulSoup and built a ranking algorithm combining user preferences (e.g., natural birth, proximity, female doctors) with hospital-level indicators.
Proposed deployment as a mobile application with an interactive React frontend for search and visualization.
Highlighted policy-level implications, enabling data-driven maternal healthcare planning and boosting trust in local hospitals.