Using machine learning techniques for exploration and classification of laboratory data

Abstract

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 analysis of laboratory data.

Keywords

• k-means
• machine learning
• principal component analysis
• random forest
• decision tree