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
- body mass index
- type diabetes
- machine learning
- community dwelling
- risk factors
- physical activity
- end stage renal disease
- healthcare
- ejection fraction
- newly diagnosed
- chronic kidney disease
- blood glucose
- middle aged
- prognostic factors
- insulin resistance
- high resolution
- patient reported outcomes
- weight gain
- skeletal muscle
- blood pressure
- adipose tissue
- mass spectrometry