Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods

Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based pr...

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Published inSensors (Basel, Switzerland) Vol. 22; no. 16; p. 6155
Main Authors Hu, Hansel, Lai, Tin, Farid, Farnaz
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
MDPI
Subjects
Accuracy
adolescent diabetes prediction
Adults
Biomarkers
Datasets
Demographics
Diabetes
diabetes detection
Disease control
Feasibility studies
Feature selection
Glucose
Laboratories
Machine learning
medical machine learning
Medical research
Medical screening
Medical tests
Medicine, Experimental
Methods
Personal information
Physical properties
Prediabetic state
Prediction models
Support vector machines
Teenagers
Type 2 diabetes
Australia
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Summary:Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based predictive model to detect adolescent diabetes. The model applies supervised machine learning and a novel feature selection method to the National Health and Nutritional Examination Survey datasets after an exhaustive search to select reliable and accurate data. The best model achieved an area under the curve (AUC) score of 71%. This research proves that a screening tool based on supervised machine learning models can assist in the automated detection of youth diabetes. It also identifies some critical predictors to such detection using Lasso Regression, Random Forest Importance and Gradient Boosted Tree Importance feature selection methods. The most contributing features to Youth diabetes detection are physical characteristics (e.g., waist, leg length, gender), dietary information (e.g., water, protein, sodium) and demographics. These predictors can be further utilised in other areas of medical research, such as electronic medical history.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22166155