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A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

Author

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  • Jihye Lim

    (Department of Healthcare Management, Youngsan University, Yangsan 626-790, Korea)

  • Jungyoon Kim

    (Department of Computer Science, Kent State University, Kent, OH 44242, USA)

  • Songhee Cheon

    (Department of Physical Therapy, Youngsan University, Yangsan 626-790, Korea)

Abstract

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve ( AUC ) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

Suggested Citation

  • Jihye Lim & Jungyoon Kim & Songhee Cheon, 2019. "A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data," IJERPH, MDPI, vol. 16(7), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1281-:d:221498
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    References listed on IDEAS

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    1. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
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    Cited by:

    1. Jungyoon Kim & Jihye Lim, 2021. "A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data," IJERPH, MDPI, vol. 18(10), pages 1-13, May.

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