RT Journal Article
T1 A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses
A1 Sánchez Rico, Marina
A1 Alvarado Izquierdo, Jesús María
AB The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.
PB MDPI
SN 2076-328X
YR 2019
FD 2019
LK https://hdl.handle.net/20.500.14352/12388
UL https://hdl.handle.net/20.500.14352/12388
LA eng
NO Universidad Complutense de Madrid/Banco de Santander
DS Docta Complutense
RD 9 abr 2025