IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i10p5386-d557111.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/10/5386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/10/5386/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    2. 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.
    3. 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.
    4. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    5. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    6. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    7. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    8. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    9. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    10. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    11. Jamil Ahmad & Abdul Khader Jilani Saudagar & Khalid Mahmood Malik & Waseem Ahmad & Muhammad Badruddin Khan & Mozaherul Hoque Abul Hasanat & Abdullah AlTameem & Mohammed AlKhathami & Muhammad Sajjad, 2022. "Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans," IJERPH, MDPI, vol. 19(1), pages 1-16, January.
    12. Rasheed Omobolaji Alabi & Alhadi Almangush & Mohammed Elmusrati & Ilmo Leivo & Antti Mäkitie, 2022. "Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication," IJERPH, MDPI, vol. 19(14), pages 1-13, July.
    13. Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    14. Vidhya V. & Anjan Gudigar & U. Raghavendra & Ajay Hegde & Girish R. Menon & Filippo Molinari & Edward J. Ciaccio & U. Rajendra Acharya, 2021. "Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives," IJERPH, MDPI, vol. 18(12), pages 1-29, June.
    15. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    16. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    17. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    18. Kai Feng & Han Hong & Ke Tang & Jingyuan Wang, 2023. "Statistical Tests for Replacing Human Decision Makers with Algorithms," Papers 2306.11689, arXiv.org.
    19. Zhiming Cui & Yu Fang & Lanzhuju Mei & Bojun Zhang & Bo Yu & Jiameng Liu & Caiwen Jiang & Yuhang Sun & Lei Ma & Jiawei Huang & Yang Liu & Yue Zhao & Chunfeng Lian & Zhongxiang Ding & Min Zhu & Dinggan, 2022. "A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    20. Chowdhury, Emon Kalyan, 2019. "Use of Artificial Intelligence in Stock Trading," MPRA Paper 118175, University Library of Munich, Germany, revised 18 Apr 2019.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5386-:d:557111. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.