In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.
Keyphrases
- type diabetes
- body mass index
- machine learning
- risk factors
- community dwelling
- end stage renal disease
- physical activity
- ejection fraction
- newly diagnosed
- chronic kidney disease
- healthcare
- insulin resistance
- middle aged
- prognostic factors
- cardiovascular disease
- glycemic control
- artificial intelligence
- adipose tissue
- big data
- mass spectrometry
- skeletal muscle
- metabolic syndrome
- deep learning