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Application of dimensionality reduction and clustering techniques for the analysis of Carrion's disease cases in the period 2000–2024
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DOI：10.3389/frai.2026.1883357.png)
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Abstract
En 中文
The heterogeneous geographic distribution and the complex dynamics of Carrion's disease challenge conventional epidemiological surveillance in Peru. To address this; this study applied unsupervised machine learning to 43; 534 national records (2000–2024). Following a rigorous data cleaning process—which resolved duplicate records; missing information; and outliers using Tukey's interquartile range (IQR)—the dimensionality reduction approaches MCA and FAMD coupled with the K-Means algorithm were evaluated. The calibration of the Silhouette; Davies–Bouldin; and Calinski–Harabasz indices determined that the combination of FAMD and K-Means provided the best clustering quality; identifying two clearly differentiated epidemiological profiles. An ablation control experiment demonstrated that; even after excluding the ICD-10 diagnostic coding; the geometric structure of the clusters remained highly stable; indicating that the temporal; geographic; and demographic variables contain sufficient information to preserve the clustering structure. This internal consistency was indicated through bootstrap resampling simulations. In conclusion; the coupling of FAMD and K-Means establishes a stable and reproducible framework for advanced exploratory epidemiology; constituting a valuable complementary tool to support public health surveillance and guide strategic decision-making in public health.
Keywords:
public health
clustering
unsupervised learning
bartonellosis
Carrión's disease
Journal
F
IF:
4.7
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2.2K
Citations:
4.4K
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