Using machine learning techniques for exploration and classification of laboratory data

The study aims to acquaint readers with six widely used machine learning (ML) techniques (Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), k-means, hierarchical clustering and the decision tree models (rpart and random forest)) that might be useful for the an...

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Autors principals:	Trulson Inga, Holdenrieder Stefan, Hoffmann Georg
Format:	Artigo
Idioma:	Inglês
Publicat:	De Gruyter 2024-10-01
Col·lecció:	Journal of Laboratory Medicine
Matèries:	k-means machine learning principal component analysis random forest decision tree
Accés en línia:	https://doi.org/10.1515/labmed-2024-0100
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