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Volume 11
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10.3390/cmsf2025011023
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Proceeding Paper

Enhancing Public Health Insights and Interpretation Through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualization of Infectious Disease Trends †

by
Ayauzhan Arystambekova
and
Eugene Pinsky
*
*
Author to whom correspondence should be addressed.
†
Presented at the 11th International Conference on Time Series and Forecasting, Canaria, Spain, 16–18 July 2025.
‡
All authors contributed equally to this work.
Comput. Sci. Math. Forum 2025, 11(1), 23; https://doi.org/10.3390/cmsf2025011023
Published: 11 August 2025
(This article belongs to the Proceedings of The 11th International Conference on Time Series and Forecasting)
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Abstract

This paper applies hierarchical clustering and Hamming Distance to analyze the temporal trends of infectious diseases across different regions of Uzbekistan. By leveraging hierarchical clustering, we effectively group regions based on disease similarity without requiring predefined cluster numbers. Hamming Distance further quantifies disease trajectory similarities, helping assess epidemiological patterns over time. Binary tree visualizations enhance the interpretability of clustering results, offering a novel method for identifying regional trends. The dataset includes yearly incidence rates of seven infectious diseases from 2012 to 2019, along with population, healthcare infrastructure, and geographic attributes for each region. This approach provides an interpretable framework for public health analysis and decision-making.
Keywords: artificial intelligence in public health; machine learning; visualization of time-series; hierarchical clustering; disease surveillance; interpretation of health patterns; decision support systems artificial intelligence in public health; machine learning; visualization of time-series; hierarchical clustering; disease surveillance; interpretation of health patterns; decision support systems

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MDPI and ACS Style

Arystambekova, A.; Pinsky, E. Enhancing Public Health Insights and Interpretation Through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualization of Infectious Disease Trends. Comput. Sci. Math. Forum 2025, 11, 23. https://doi.org/10.3390/cmsf2025011023

AMA Style

Arystambekova A, Pinsky E. Enhancing Public Health Insights and Interpretation Through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualization of Infectious Disease Trends. Computer Sciences & Mathematics Forum. 2025; 11(1):23. https://doi.org/10.3390/cmsf2025011023

Chicago/Turabian Style

Arystambekova, Ayauzhan, and Eugene Pinsky. 2025. "Enhancing Public Health Insights and Interpretation Through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualization of Infectious Disease Trends" Computer Sciences & Mathematics Forum 11, no. 1: 23. https://doi.org/10.3390/cmsf2025011023

APA Style

Arystambekova, A., & Pinsky, E. (2025). Enhancing Public Health Insights and Interpretation Through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualization of Infectious Disease Trends. Computer Sciences & Mathematics Forum, 11(1), 23. https://doi.org/10.3390/cmsf2025011023

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