Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.
Mingyue XueYinxia SuChen LiShuxia WangHua YaoPublished in: Journal of diabetes research (2020)
We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables' importance scores gives a clue to prevent diabetes occurrence.
Keyphrases
- type diabetes
- glycemic control
- cardiovascular disease
- machine learning
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- risk assessment
- human health
- patient safety
- peritoneal dialysis
- prognostic factors
- high throughput
- artificial intelligence
- patient reported outcomes
- insulin resistance
- climate change
- quality improvement