Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years' Data.
Jing LiZheng XuTeng-Da XuSongbai LinPublished in: Diabetes, metabolic syndrome and obesity : targets and therapy (2022)
This study demonstrated improved performance with the accumulation of longitudinal data when using machine learning for diabetes prediction in MetS patients. For individuals with similar clinical parameters, the variation trends of these parameters could change the risk of future diabetes. This result indicated that models based on longitudinal multiple years' data may provide more personalized assessment tools for risk evaluation.
Keyphrases
- type diabetes
- cardiovascular disease
- metabolic syndrome
- electronic health record
- glycemic control
- end stage renal disease
- big data
- newly diagnosed
- ejection fraction
- cross sectional
- chronic kidney disease
- insulin resistance
- peritoneal dialysis
- data analysis
- machine learning
- skeletal muscle
- adipose tissue
- clinical evaluation
- patient reported