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A Comparative Evaluation Of The Generalised Predictive Ability Of Eight Machine Learning Algorithms Across Ten Clinical Metabolomics Data Sets For Binary Classification, Kevin M. Mendez, Stacey N. Reinke, David I. Broadhurst
A Comparative Evaluation Of The Generalised Predictive Ability Of Eight Machine Learning Algorithms Across Ten Clinical Metabolomics Data Sets For Binary Classification, Kevin M. Mendez, Stacey N. Reinke, David I. Broadhurst
Research outputs 2014 to 2021
Introduction:
Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models.
Objectives:
We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when …