A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

Sánchez Rico, MarinaAlvarado Izquierdo, Jesús María2023-06-172023-06-1720192076-328X10.3390/bs9120122https://hdl.handle.net/20.500.14352/12388The 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.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/A Machine Learning Approach for Studying the Comorbidities of Complex Diagnosesjournal articlehttps://doi.org/10.3390/bs9120122open accessComorbiditiesdepressionUMAPhierarchical clusteringPsicología (Psicología)61 Psicología