An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction

Abstract:Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide. Electronic health records of patients diagnosed with this disease can serve as valuable datasets for computational analyses, enabling the discovery of new insights about the pathology. Unsupervised clustering, in particular, can identify groups of patients with medically significant features, revealing data trends that might otherwise go unnoticed by medical doctors. In this study, we first applied the DBSCAN density-based clustering method to three independent datasets derived from electronic medical records of patients with mammary carcinoma. Subsequently, to enhance our results, we preceded the DBSCAN application with a dimensionality reduction phase using UMAP. We evaluated our clustering outcomes using three statistical indices (DBCV, DCSI, and DISCO). Our results confirm the effectiveness of combining UMAP with DBSCAN for clustering data derived from electronic health records, paving the way for the medical interpretation of the patient groups identified by our approach.

Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2607.19089 [cs.LG]

(or arXiv:2607.19089v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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