Predicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms

Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-10, Vol.11 (20), p.4388
Main Authors: Neamtu, Bogdan, Negrea, Mihai Octavian, Neagu, Iuliana
Format: Article
Language:English
Subjects:
Age groups
Algorithms
Analysis
binary regression
Bivariate analysis
Body mass index
Body size
CART decision trees
Cholesterol
Cluster analysis
Clustering
Data collection
Data mining
Decision trees
Diabetes
Diabetes therapy
Disease control
Education
Family income
Glucose
glycemic control
Glycosylated hemoglobin
Hypotheses
Insulin
Life expectancy
Machine learning
Patients
pediatric type 1 diabetes
Pediatrics
Predictive control
Regression analysis
Risk factors
Social factors
Socioeconomic factors
Standard scores
Statistical methods
Subgroups
Thyroid gland
Triglycerides
two-step cluster analysis
Type 1 diabetes
Variables
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Summary:Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a challenging task. The American Association of Diabetes (ADA), the Pediatric Endocrine Society, and the International Diabetes Federation (ISPAD) recommend the adoption of a harmonized A1C of
ISSN:2227-7390
2227-7390
DOI:10.3390/math11204388