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A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data

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  • Jungyoon Kim

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

  • Jihye Lim

    (Department of Health Care and Science, Donga University, Nakdong-Daero 550 beongil 37, Saha-Gu, Busan 49315, Korea)

Abstract

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5386-:d:557111
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    References listed on IDEAS

    as
    1. Jung H. Lee & Kang U. Lee & Dong Y. Lee & Ki W. Kim & Jin H. Jhoo & Ju H. Kim & Kun H. Lee & Sung Y. Kim & Sul H. Han & Jong I. Woo, 2002. "Development of the Korean Version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-K)," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 57(1), pages 47-53.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Igor O Korolev & Laura L Symonds & Andrea C Bozoki & Alzheimer's Disease Neuroimaging Initiative, 2016. "Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-25, February.
    4. 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.
    5. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    6. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.
    7. Chris Allen & Ming-Hsiang Tsou & Anoshe Aslam & Anna Nagel & Jean-Mark Gawron, 2016. "Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-10, July.
    8. Qinneng Xu & Yulia R Gel & L Leticia Ramirez Ramirez & Kusha Nezafati & Qingpeng Zhang & Kwok-Leung Tsui, 2017. "Forecasting influenza in Hong Kong with Google search queries and statistical model fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
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