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.
J Diabetes Sci Technol
; 14(2): 297-302, 2020 03.
Article
in En
| MEDLINE
| ID: mdl-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.METHODS:
We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods.RESULTS:
The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%).CONCLUSION:
The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Blood Glucose
/
Health Status Indicators
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Glucose Intolerance
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Diabetes Mellitus, Type 2
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Support Vector Machine
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Evaluation_studies
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Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Language:
En
Year:
2020
Type:
Article