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
Preview |
Text
Forssen_Evaluation_Machine_Learning_Methods.pdf - Published Version Download (309kB) | Preview |
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.
Archive Staff Only
 |
View Item |
