Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques

Jon Kerexeta, Arkaitz Artetxe, Vanessa Escolar, Ainara Lozano, Nekane Larburu

2018

Abstract

Heart Failure (HF) is a syndrome that reduces patients’ quality of life, and has severe impacts on healthcare systems worldwide, such as the high rate of readmissions. In order to reduce the readmissions and improve patients’ quality of life, several studies are trying to assess the risk of a patient to be readmitted, so that taking right actions clinicians can prevent patient deterioration and readmission. Predictive models have the ability to identify patients at high risk. Henceforth, this paper studies predictive models to determine the risk of a HF patient to be readmitted in the next 30 days after discharge. We present two different approaches. In the first one, we combine unsupervised and supervised classification and achieved AUC score of 0.64. In the second one, we combine decision tree and Naïve Bayes classifiers and achieved AUC score of 0.61. Additionally, we discover that the results improve when training the predictive models with different readmission’s threshold outcome, reaching the AUC score of 0.73 when applying the first approach.

Paper Citation

in Harvard Style

Kerexeta J., Artetxe A., Escolar V., Lozano A. and Larburu N. (2018). Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF; ISBN 978-989-758-281-3, SciTePress, pages 308-315. DOI: 10.5220/0006542103080315

in Bibtex Style

@conference{healthinf18,
author={Jon Kerexeta and Arkaitz Artetxe and Vanessa Escolar and Ainara Lozano and Nekane Larburu},
title={Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF},
year={2018},
pages={308-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006542103080315},
isbn={978-989-758-281-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF
TI - Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques
SN - 978-989-758-281-3
AU - Kerexeta J.
AU - Artetxe A.
AU - Escolar V.
AU - Lozano A.
AU - Larburu N.
PY - 2018
SP - 308
EP - 315
DO - 10.5220/0006542103080315
PB - SciTePress