Cargando…
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...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416136/ https://www.ncbi.nlm.nih.gov/pubmed/36015915 http://dx.doi.org/10.3390/s22166155 |
_version_ | 1784776406202318848 |
---|---|
author | Hu, Hansel Lai, Tin Farid, Farnaz |
author_facet | Hu, Hansel Lai, Tin Farid, Farnaz |
author_sort | Hu, Hansel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9416136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94161362022-08-27 Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods Hu, Hansel Lai, Tin Farid, Farnaz Sensors (Basel) Article 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. MDPI 2022-08-17 /pmc/articles/PMC9416136/ /pubmed/36015915 http://dx.doi.org/10.3390/s22166155 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Hansel Lai, Tin Farid, Farnaz Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title | Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title_full | Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title_fullStr | Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title_full_unstemmed | Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title_short | Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods |
title_sort | feasibility study of constructing a screening tool for adolescent diabetes detection applying machine learning methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416136/ https://www.ncbi.nlm.nih.gov/pubmed/36015915 http://dx.doi.org/10.3390/s22166155 |
work_keys_str_mv | AT huhansel feasibilitystudyofconstructingascreeningtoolforadolescentdiabetesdetectionapplyingmachinelearningmethods AT laitin feasibilitystudyofconstructingascreeningtoolforadolescentdiabetesdetectionapplyingmachinelearningmethods AT faridfarnaz feasibilitystudyofconstructingascreeningtoolforadolescentdiabetesdetectionapplyingmachinelearningmethods |