Identifying key features for determining the patterns of patients with functional dyspepsia using machine learning
Through supervised learning, we identified the most important features for PI. Additionally, we proposed a novel unsupervised learning approach for identifying patterns from the patient data. This study could facilitate clinical decision making as it pertains to patients with FD.
Authors
- In‐Seon Lee (ORCID: https://orcid.org/0000-0001-9275-3111)
- Da-Eun Yoon (ORCID: https://orcid.org/0000-0002-6718-273X)
- Younkuk Choi (ORCID: https://orcid.org/0009-0005-1206-4989)
- Younbyoung Chae (ORCID: https://orcid.org/0000-0001-6787-2215)
- Heeyoung Moon (ORCID: https://orcid.org/0000-0001-6770-4166)
- Junsuk Kim
- Heekyung Kim (ORCID: https://orcid.org/0000-0002-8543-0792)
Institutions
- Kwangwoon University (KR)
- Kyung Hee University (KR)
- Semyung University (KR)
- Shingu College (KR)
- Dr Youth Clinic (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-10-10
- DOI
- https://doi.org/10.3389/fphys.2025.1658866
- Primary Topic
- Gastrointestinal motility and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00