The Clustering Analysis on Public Health Data with Missing Values Based on Dimension Reduction Methods
- DOI
- 10.2991/978-94-6463-242-2_7How 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.
Cite this article
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 -