Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Inui, Atsuyuki, Nishimoto, Hanako, Mifune, Yutaka, Yoshikawa, Tomoya, Shinohara, Issei, Furukawa, Takahiro, Kato, Tatsuo, Tanaka, Shuya, Kusunose, Masaya, Kuroda, Ryosuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1784913512541192192
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
work_keys_str_mv AT inuiatsuyuki screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT nishimotohanako screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT mifuneyutaka screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT yoshikawatomoya screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT shinoharaissei screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT furukawatakahiro screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT katotatsuo screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT tanakashuya screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT kusunosemasaya screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach
AT kurodaryosuke screeningforosteoporosisfrombloodtestdatainelderlywomenusingamachinelearningapproach