Determinants and prediction of hypertension among Chinese middle-aged and elderly adults with diabetes: A machine learning approach
ML algorithms showed accurate predictive capabilities for DCH. Overall, non-linear ML models outperformed traditional logistic regression for predicting DCH. DCH predictions exhibited variations in predictors and model accuracy by gender. These findings could help identify DCH early and inform the development of personalized intervention strategies.
Authors
- Hailei Zhao (ORCID: https://orcid.org/0000-0001-5564-3677)
- Luotao Lin (ORCID: https://orcid.org/0000-0002-4817-2392)
- Xianglong Xu (ORCID: https://orcid.org/0000-0003-1873-4734)
- Zumin Shi (ORCID: https://orcid.org/0000-0002-3099-3299)
- Hualing Song (ORCID: https://orcid.org/0000-0002-0589-2904)
- Lijun Mao (ORCID: https://orcid.org/0009-0006-8966-8665)
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- Melbourne Sexual Health Centre (AU)
- Individual Differences (US)
- Alfred Health (AU)
- Monash University (AU)
- Qatar University (QA)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e38124
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.42
Funders
- Shanghai Municipal Health Commission
- Shanghai University of Traditional Chinese Medicine