ArticleFrontiers in artificial intelligence2026
Application of dimensionality reduction and clustering techniques for the analysis of Carrion's disease cases in the period 2000-2024.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.