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Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach
The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045086/ https://www.ncbi.nlm.nih.gov/pubmed/36978668 http://dx.doi.org/10.3390/bioengineering10030277 |
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author | Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kusunose, Masaya Kuroda, Ryosuke |
author_facet | Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kusunose, Masaya Kuroda, Ryosuke |
author_sort | Inui, Atsuyuki |
collection | PubMed |
description | The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research. |
format | Online Article Text |
id | pubmed-10045086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100450862023-03-29 Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kusunose, Masaya Kuroda, Ryosuke Bioengineering (Basel) Article The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research. MDPI 2023-02-21 /pmc/articles/PMC10045086/ /pubmed/36978668 http://dx.doi.org/10.3390/bioengineering10030277 Text en © 2023 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 Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kusunose, Masaya Kuroda, Ryosuke Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title | Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title_full | Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title_fullStr | Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title_full_unstemmed | Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title_short | Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach |
title_sort | screening for osteoporosis from blood test data in elderly women using a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045086/ https://www.ncbi.nlm.nih.gov/pubmed/36978668 http://dx.doi.org/10.3390/bioengineering10030277 |
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