An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach
Utilizing machine learning techniques, our predictive model can precisely evaluate the risk of anxiety symptoms among elderly individuals with abdominal obesity, facilitating the timely adoption of targeted intervention measures. The integration of XGBoost and SHAP offers transparent interpretations for customized risk forecasts.
Authors
- Zitong Zhang (ORCID: https://orcid.org/0000-0003-1513-3044)
- Xiaobing Xian
- Jingyu Cheng
- Rui Xue (ORCID: https://orcid.org/0000-0002-5710-0091)
- Shiwei Cao (ORCID: https://orcid.org/0000-0003-0071-7151)
- Jingxi Ma (ORCID: https://orcid.org/0000-0001-9189-4684)
- Qian Ran
- Liang Yu (ORCID: https://orcid.org/0009-0007-3922-3454)
- Tengfei Niu
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Third People 's Hospital of Chongqing (CN)
- Chongqing Public Health Medical Center (CN)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical and Pharmaceutical College (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-12-10
- DOI
- https://doi.org/10.3389/fpsyt.2024.1451703
- Citations
- 5
- Primary Topic
- Psychological Well-being and Life Satisfaction
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- Chongqing Municipal Education Commission