Version 1
: Received: 21 May 2024 / Approved: 22 May 2024 / Online: 22 May 2024 (10:49:31 CEST)
How to cite:
Khamis, G. S. M.; Al Qahtani, N. S.; Alanazi, S. M.; Alruwaili, M. M.; Alenazi, M. S.; Alruwaili, M. A. Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity. Preprints2024, 2024051425. https://doi.org/10.20944/preprints202405.1425.v1
Khamis, G. S. M.; Al Qahtani, N. S.; Alanazi, S. M.; Alruwaili, M. M.; Alenazi, M. S.; Alruwaili, M. A. Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity. Preprints 2024, 2024051425. https://doi.org/10.20944/preprints202405.1425.v1
Khamis, G. S. M.; Al Qahtani, N. S.; Alanazi, S. M.; Alruwaili, M. M.; Alenazi, M. S.; Alruwaili, M. A. Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity. Preprints2024, 2024051425. https://doi.org/10.20944/preprints202405.1425.v1
APA Style
Khamis, G. S. M., Al Qahtani, N. S., Alanazi, S. M., Alruwaili, M. M., Alenazi, M. S., & Alruwaili, M. A. (2024). Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity. Preprints. https://doi.org/10.20944/preprints202405.1425.v1
Chicago/Turabian Style
Khamis, G. S. M., Mariam Shabram Alenazi and Maneaf Afet Alruwaili. 2024 "Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity" Preprints. https://doi.org/10.20944/preprints202405.1425.v1
Abstract
Cardiovascular disease (CVD) and obesity are prevalent public health concerns with grave implications for morbidity and mortality, necessitating tailored interventions. This research explores the utility of fuzzy c-means clustering paired with principal component analysis (PCA) in detecting at-risk groups and personalizing health strategies for these conditions. Fuzzy c-means clustering allows for the dynamic classification of individuals into groups based on unique risk factors and intervention outcomes, while PCA aids in distilling complex data sets to uncover underlying patterns. The conjoined use of these methods has shown promise in identifying diverse risk profiles and in forecasting intervention success rates. The study acknowledges limitations, including possible biases stemming from data set composition and analytical parameter selection. Future research aims to refine these tools for clinical application. The results support implementing fuzzy c-means clustering and PCA for delineating specific target populations for health interventions, emphasizing careful use of these analytical approaches. Subsequent studies should focus on correlating these techniques with concrete clinical results to enhance public health measures.
Keywords
Fuzzy C means; PCA; machine learning; obesity; CVD
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
