Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.
Journal:
Journal of diabetes science and technology
PMID:
30931604
Abstract
BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices.
Authors
Keywords
Adult
Aged
Algorithms
Blood Glucose
Blood Glucose Self-Monitoring
Data Interpretation, Statistical
Datasets as Topic
Diabetes Mellitus, Type 2
Diagnosis, Differential
Female
Glucose Intolerance
Glycemic Control
Health Status Indicators
Humans
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Support Vector Machine