The Clustering Analysis on Public Health Data with Missing Values Based on Dimension Reduction Methods

Honghao Zhao; Weiyi Ding; Fang Ye; Weimeng Yuan; Hangyu Chen

doi:10.2991/978-94-6463-242-2_7

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The Clustering Analysis on Public Health Data with Missing Values Based on Dimension Reduction Methods

Authors

Honghao Zhao¹^{, *}, Weiyi Ding¹, Fang Ye¹, Weimeng Yuan¹, Hangyu Chen¹

¹Department of Decision Sciences, School of Business, Macau University of Science and Technology, Taipa, Macao, China

^*Corresponding author. Email: hhzhao@must.edu.mo

Corresponding Author

Honghao Zhao

Available Online 22 September 2023.

DOI: 10.2991/978-94-6463-242-2_7 How to use a DOI?
Keywords: Public Health Data; Clustering Analysis; Matrix Completion; Dimension Reduction
Abstract: With the development of medical information digitization, machine learning techniques have become a popular method of mining medical health data for hidden information and knowledge. Health data from normal medical checking is usually limited. However, public health data from magnetic resonance (MR) are usually high-dimensional data with missing values. This paper presents a clustering analysis of such health data with a series of steps, including filling missing values, dimension reduction, and clustering to provide a framework with potential solutions to the problems of missing value and high data dimension. Our results show that the UMAP method is the most effective one for dimension reduction, and the K-means clustering method works well in most cases.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
Series: Atlantis Highlights in Computer Sciences
Publication Date: 22 September 2023
ISBN: 10.2991/978-94-6463-242-2_7
ISSN: 2589-4900
DOI: 10.2991/978-94-6463-242-2_7 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Honghao Zhao
AU  - Weiyi Ding
AU  - Fang Ye
AU  - Weimeng Yuan
AU  - Hangyu Chen
PY  - 2023
DA  - 2023/09/22
TI  - The Clustering Analysis on Public Health Data with Missing Values Based on Dimension Reduction Methods
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 45
EP  - 51
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_7
DO  - 10.2991/978-94-6463-242-2_7
ID  - Zhao2023
ER  -

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