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A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

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Sánchez Rico, Marina and Alvarado Izquierdo, Jesús María (2019) A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses. Behavioral Sciences, 9 (12). p. 122. ISSN 2076-328X

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Official URL: https://doi.org/10.3390/bs9120122



Abstract

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.


Item Type:Article
Uncontrolled Keywords:Comorbidities; depression; UMAP; hierarchical clustering
Subjects:Medical sciences > Psychology
ID Code:62047
Deposited On:10 Sep 2020 07:09
Last Modified:10 Sep 2020 09:55

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