Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data

2017/02/28 by Henrietta Forssen, Riyaz Patel, Forssen, Henrietta +14
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Metabolomics and Mass Spectrometry Studies #cs.LG

paper · pdf · doi:10.48550/arxiv.1703.02116

Medical Informatics Europe (MIE2017)

arxiv created 2017/02/28 · openalex publication_date 2017/02/28 · arxiv updated 2017/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poorer accuracy. Supervised machine learning methods can potentially be used in order to fully exploit the dimensionality and richness of the data. In this paper, we systematically implement and evaluate a set of supervised learning methods (L1 regression, random forest classifier) and compare them to traditional regression-based approaches for disease prediction using metabolomic data.

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