Name
Title | ||
|---|---|---|
Machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction |
Abstract | ||
|---|---|---|
Endothelial Dysfunction is achieving increasing importance, because it is strictly related to cardiovascular risks and it provides important prognostic data in addition to the classical ones. This paper introduces a machine learning approach for predicting Endothelial Dysfunction. The approach was applied and tested on a newly collected dataset, “Endothelial Dysfunction Dataset (EDD)” and several machine learning algorithms are compared. This method comprises features related to the anthropometric or pathological characteristics of the analysed subjects. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MESA.2018.8449152 | 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) |
Keywords | Field | DocType |
machine learning-based approaches,machine learning approach,Endothelial Dysfunction Dataset,prognostic data,cardiovascular risks | Computer science,Support vector machine,Artificial intelligence,Endothelial dysfunction,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-4644-1 | 0 | 0.34 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chiara Calamanti | 1 | 0 | 0.34 |
| Marina Paolanti | 2 | 10 | 12.39 |
| luca romeo | 3 | 21 | 9.59 |
| Michele Bernardini | 4 | 2 | 3.07 |
| Emanuele Frontoni | 5 | 248 | 47.04 |