Name
Title | ||
|---|---|---|
Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-753-5-111 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
coronary artery disease,random forest,machine learning,EHR | Coronary artery disease,Data mining,Regression,Computer science,A priori and a posteriori,Metabolomics,Supervised learning,Curse of dimensionality,Exploit,Artificial intelligence,Random forest,Machine learning | Journal |
Volume | ISSN | Citations |
235 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henrietta Forssen | 1 | 0 | 0.34 |
| Riyaz S. Patel | 2 | 0 | 0.34 |
| Natalie Fitzpatrick | 3 | 1 | 1.64 |
| Hingorani Aroon D | 4 | 1 | 1.30 |
| Adam Timmis | 5 | 0 | 0.34 |
| Harry Hemingway | 6 | 19 | 5.72 |
| Spiros Denaxas | 7 | 4 | 6.43 |