Decision tree learning for predicting chronic postpartum depression in the Japan Environment and Children's Study
This study created a simple and relatively high-performing prediction model. Because the model can be easily understood and used without expertise in machine learning, it is expected to be useful in maternal health settings, including grassroots community health.
Authors
- Kei Hamazaki (ORCID: https://orcid.org/0000-0003-0456-6805)
- Hidekuni Inadera (ORCID: https://orcid.org/0000-0002-3548-221X)
- Haruka Kasamatsu
- Akiko Tsuchida (ORCID: https://orcid.org/0000-0003-1209-3104)
- Kenta Matsumura (ORCID: https://orcid.org/0000-0002-4271-1543)
Institutions
- Gunma University (JP)
- University of Toyama (JP)
Publication Details
- Journal
- Journal of Affective Disorders
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1016/j.jad.2024.10.034
- Citations
- 6
- Primary Topic
- Maternal Mental Health During Pregnancy and Postpartum
- Type
- article
- Field-Weighted Citation Impact
- 3.26
Funders
- Ministry of the Environment, Government of Japan