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Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study
OBJECTIVES: To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. M...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Galenos Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286847/ https://www.ncbi.nlm.nih.gov/pubmed/37345311 http://dx.doi.org/10.4274/tjo.galenos.2022.36724 |
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author | Bulut, Erkan Köprübaşı, Sümeyra Dayi, Özlem Bulut, Hatice |
author_facet | Bulut, Erkan Köprübaşı, Sümeyra Dayi, Özlem Bulut, Hatice |
author_sort | Bulut, Erkan |
collection | PubMed |
description | OBJECTIVES: To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. MATERIALS AND METHODS: Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis. RESULTS: The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively. CONCLUSION: Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children. |
format | Online Article Text |
id | pubmed-10286847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Galenos Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-102868472023-06-23 Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study Bulut, Erkan Köprübaşı, Sümeyra Dayi, Özlem Bulut, Hatice Turk J Ophthalmol Original Article OBJECTIVES: To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. MATERIALS AND METHODS: Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis. RESULTS: The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively. CONCLUSION: Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children. Galenos Publishing 2023-06 2023-06-21 /pmc/articles/PMC10286847/ /pubmed/37345311 http://dx.doi.org/10.4274/tjo.galenos.2022.36724 Text en © Copyright 2023 by Turkish Ophthalmological Association | Turkish Journal of Ophthalmology, published by Galenos Publishing House. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Bulut, Erkan Köprübaşı, Sümeyra Dayi, Özlem Bulut, Hatice Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title | Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title_full | Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title_fullStr | Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title_full_unstemmed | Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title_short | Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study |
title_sort | clinical relevance of choroidal thickness in obese and healthy children: a machine learning study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286847/ https://www.ncbi.nlm.nih.gov/pubmed/37345311 http://dx.doi.org/10.4274/tjo.galenos.2022.36724 |
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