Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data
Evidence from CHNS
Bibliographic Data
| ID | 15375186 |
|---|---|
| Authors | Xiaolong Li (0000-0002-1674-9345, Ningxia Medical University, corresponding author), Fan Ding (0000-0001-5784-0228, Ningxia Medical University), Lu Zhang (0000-0002-8855-8492, Ningxia Medical University), Shi Zhao (0000-0001-8722-6149, Tianjin Medical University), Zengyun Hu (0000-0002-4259-9141, Shanghai Jiao Tong University), Zhanbing Ma (0000-0002-3001-2980, Ningxia Medical University), Feng Li (0000-0002-6589-6392), Li Feng (0000-0002-2709-3628, Ningxia Medical University), Yuhong Zhang (0000-0002-1450-1098, Ningxia Medical University), Yi Zhao (0009-0002-1840-3978), Yiyi Zhao (0000-0002-8766-1383, Ningxia Medical University), Yu Zhao (0000-0001-9446-644X, Ningxia Medical University) |
| Year | 2025 |
| Volume | 25 |
| Issue | 1 |
| Pages | 1145-1145 |
| Publication date | 2025-03-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-025-22419-7 |
| PMID | 40140819 |
| OpenAlex | W4408840728 |
| Language | EN |
| Citations received | 1 |
| References cited | 60 |
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM
Artificial neural network · Diabetes mellitus · Interpretability · Lasso (programming language · Logistic regression · Machine learning · Naive Bayes classifier · Public health · Random forest · Support vector machine · Artificial Intelligence in Healthcare · Computer Science · Diabetes, Cardiovascular Risks, and Lipoproteins · Machine Learning in Healthcare · Medicine · Artificial Intelligence · Endocrinology
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |