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

Forssen, H; Patel, R; Fitzpatrick, N; Hingorani, A; Timmis, A; Hemingway, H; Denaxas, S; (2017) Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data. In: Randell, R and Cornet, R and McCowan, C and Peek, N and Scott, PJ, (eds.) Informatics for Health: Connected Citizen-Led Wellness and Population Health. (pp. 111-115). IOS Press Green open access

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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.

Type: Book chapter
Title: Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data
Location: Netherlands
ISBN-13: 9781614997528
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/978-1-61499-753-5-111
Publisher version: http://ebooks.iospress.com/volumearticle/46312
Language: English
Additional information: Copyright © 2017 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0) (http://creativecommons.org/licenses/by-nc/4.0/deed.en_US)
Keywords: EHR, coronary artery disease, machine learning, random forest
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/1546025
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