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Bioengineering
Volume 10
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10.3390/bioengineering10030277
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Article

Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach

by
Atsuyuki Inui
1,*,
Hanako Nishimoto
1,2,
Yutaka Mifune
1,
Tomoya Yoshikawa
1,
Issei Shinohara
1,
Takahiro Furukawa
1,
Tatsuo Kato
1,
Shuya Tanaka
1,
Masaya Kusunose
1 and
Ryosuke Kuroda
1
1
Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
2
Orthopaedic Surgery Kobe Rosai Hospital, Kagoike-dori 4-1-23, Chuou-ku, Kobe City 651-0053, Japan
*
Author to whom correspondence should be addressed.
Bioengineering 2023, 10(3), 277; https://doi.org/10.3390/bioengineering10030277
Submission received: 31 December 2022 / Revised: 12 February 2023 / Accepted: 16 February 2023 / Published: 21 February 2023
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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Abstract

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.
Keywords: machine learning; osteoporosis; artificial intelligence machine learning; osteoporosis; artificial intelligence
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MDPI and ACS Style

Inui, A.; Nishimoto, H.; Mifune, Y.; Yoshikawa, T.; Shinohara, I.; Furukawa, T.; Kato, T.; Tanaka, S.; Kusunose, M.; Kuroda, R. Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach. Bioengineering 2023, 10, 277. https://doi.org/10.3390/bioengineering10030277

AMA Style

Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I, Furukawa T, Kato T, Tanaka S, Kusunose M, Kuroda R. Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach. Bioengineering. 2023; 10(3):277. https://doi.org/10.3390/bioengineering10030277

Chicago/Turabian Style

Inui, Atsuyuki, Hanako Nishimoto, Yutaka Mifune, Tomoya Yoshikawa, Issei Shinohara, Takahiro Furukawa, Tatsuo Kato, Shuya Tanaka, Masaya Kusunose, and Ryosuke Kuroda. 2023. "Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach" Bioengineering 10, no. 3: 277. https://doi.org/10.3390/bioengineering10030277

APA Style

Inui, A., Nishimoto, H., Mifune, Y., Yoshikawa, T., Shinohara, I., Furukawa, T., Kato, T., Tanaka, S., Kusunose, M., & Kuroda, R. (2023). Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach. Bioengineering, 10(3), 277. https://doi.org/10.3390/bioengineering10030277

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