Loading
Viral Infections and Vectors / Data Driven Disease Surveillance · Journal article
Frontiers in Artificial Intelligence · August 11, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an exploratory analysis applying unsupervised machine learning (FAMD and K-Means clustering) to 43,534 Carrion's disease surveillance records to identify epidemiological profiles. The method demonstrated internal stability and reproducibility through bootstrap resampling and ablation testing, but the work is descriptive and does not validate clinical utility or prospective predictive performance.
Unsupervised machine learning analysis of surveillance data with internal validation. Peru national surveillance records for Carrion's disease, 2000–2024; no individual eligibility criteria specified. Intervention: FAMD and K-Means clustering with internal validation (bootstrap resampling, ablation controls). Compared with: MCA and FAMD coupled with K-Means were evaluated and compared; FAMD with K-Means selected as best performing. n = 43,534. Peru.
FAMD coupled with K-Means provided the best clustering quality by Silhouette, Davies–Bouldin, and Calinski–Harabasz indices Two clearly differentiated epidemiological profiles identified Ablation control excluding ICD-10 coding preserved cluster geometric structure, indicating temporal, geographic, and demographic variables contain sufficient information
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This work establishes an exploratory framework for epidemiological profiling of Carrion's disease but does not yet demonstrate clinical decision-making utility. Clinicians and public health practitioners should treat these profiles as a research-stage tool pending external validation and prospective testing.
An exploratory machine learning analysis of surveillance data using unsupervised methods to identify epidemiological profiles; methodologically sound for its class but lacks clinical validation, prospective application, or outcome prediction.
As stated by the source record.
Quoted from the source exactly as published.
This work establishes an exploratory framework for epidemiological profiling of Carrion's disease but does not yet demonstrate clinical decision-making utility. Clinicians and public health practitioners should treat these profiles as a research-stage tool pending external validation and prospective testing.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.