Vassiliki I. Kigka, Eleni I. Georga, Antonis I. Sakellarios, Nikolaos S. Tachos, Ioannis O. Andrikos, Panagiota Tsompou, Silvia Rocchiccioli, Gualtiero Pelosi, Oberdan Parodi, Lampros K. Michalis, Dimitrios I. Fotiadis. A Machine Learning Approach for the Prediction of the Progression of Cardiovascular Disease based on Clinical and Non-Invasive Imaging Data. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA, July 18-21, 2018. pages 6108-6111, IEEE, 2018. [doi]
@inproceedings{KigkaGSTATRPPMF18,
title = {A Machine Learning Approach for the Prediction of the Progression of Cardiovascular Disease based on Clinical and Non-Invasive Imaging Data},
author = {Vassiliki I. Kigka and Eleni I. Georga and Antonis I. Sakellarios and Nikolaos S. Tachos and Ioannis O. Andrikos and Panagiota Tsompou and Silvia Rocchiccioli and Gualtiero Pelosi and Oberdan Parodi and Lampros K. Michalis and Dimitrios I. Fotiadis},
year = {2018},
doi = {10.1109/EMBC.2018.8513620},
url = {https://doi.org/10.1109/EMBC.2018.8513620},
researchr = {https://researchr.org/publication/KigkaGSTATRPPMF18},
cites = {0},
citedby = {0},
pages = {6108-6111},
booktitle = {40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA, July 18-21, 2018},
publisher = {IEEE},
isbn = {978-1-5386-3646-6},
}