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A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 449))

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Abstract

A growing number of medical studies have used deep learning and machine learning for the modeling and early prediction of cardiovascular disease (CVD) risk. Modern hospitals have constructed sizeable medical data sets to predict abnormal blood pressure (BP), abnormal heart vessels, and other cardiac indicators. However, hypertension has also been demonstrated to be a risk factor for cardiovascular disease and stroke. In this paper, machine learning-based and statistic-based approaches were applied to medical data to significantly identify the disease to prevent serious illness. Furthermore, lightweight BP monitoring devices that can be used at home have enabled regular BP monitoring to predict CVD risks for early treatment.

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Acknowledgement

This work was supported in parts by Ministry of Science and Technology (MOST), Taiwan, under Grant Number MOST 110-2222-E-001-002, 110-2221-E-002-078-MY2, and 110-2321-B-075-002.

Author information

Authors and Affiliations

  1. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Chiu-Han Hsiao & Yennun Huang

  2. Department of Mathematics, National Taiwan Normal University, Taipei, Taiwan

    Po-Chun Yu

  3. Department of Information Management, National Taiwan University, Taipei, Taiwan

    Chia-Ying Hsieh, Bing-Zi Zhong, Yu-Ling Tsai & Frank Yeong-Sung Lin

  4. Center for Evidence-Based Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

    Hao-min Cheng & Wei-Lun Chang

Authors
  1. Chiu-Han Hsiao
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  2. Po-Chun Yu
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  3. Chia-Ying Hsieh
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  4. Bing-Zi Zhong
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  5. Yu-Ling Tsai
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  6. Hao-min Cheng
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  7. Wei-Lun Chang
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  8. Frank Yeong-Sung Lin
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  9. Yennun Huang
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Corresponding author

Correspondence to Chiu-Han Hsiao .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Hsiao, CH. et al. (2022). A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_32

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