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Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means
Apiwat Budwong1, Sansanee Auephanwiriyakul2, Nipon Theera-Umpon3
1Department of Computer Engineering, Faculty of Engineering, Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand.
Summary
A new algorithm, sgNERF-CM, improves infectious disease analysis in Thailand. It identifies high-risk groups for dengue, influenza, and Hepatitis B virus (HBV) based on age and occupation, aiding prevention policy development.
Area of Science:
- Epidemiology and statistical analysis in infectious diseases.
- Development of novel data mining algorithms for public health.
- Application of computational methods in disease surveillance.
Background:
- Statistical analysis is crucial for developing effective infectious disease prevention policies.
- Understanding disease relationships based on demographics and geography is essential for epidemiology.
- Existing clustering algorithms may not fully capture complex disease patterns.
Purpose of the Study:
- To develop and evaluate a novel algorithm, string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM), for analyzing infectious disease data.
- To identify relationships within dengue fever, influenza, and Hepatitis B virus (HBV) infection data in Thailand.
- To compare the performance of sgNERF-CM against other relational clustering algorithms.
Main Methods:
- Development of the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm.
- Analysis of infectious disease data (dengue, influenza, HBV) from Thailand, considering age, occupation, and region.
- Utilizing Dunn's index for optimal model selection.
- Comparative analysis with string grammar relational hard C-means (sgRHCM), relational hard C-means (RHCM), and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms.
Main Results:
- The sgNERF-CM algorithm significantly outperformed its numerical counterparts (RHCM, NERF-CM) and showed improvement over sgRHCM for most analyses.
- Month-based data provided limited value for relationship finding due to year-round disease prevalence.
- Identified specific age groups and occupations at higher risk for dengue, influenza, and HBV across different regions of Thailand.
Conclusions:
- The sgNERF-CM algorithm is a superior tool for analyzing infectious disease epidemiology and identifying at-risk populations.
- Findings highlight distinct risk profiles for dengue, influenza, and HBV based on age and occupation in Thailand.
- The study provides valuable insights for targeted public health interventions and prevention strategies.
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