Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital

Oscar Tamburis, Elio Masciari, Christian Esposito, Gerardo Fatone

2021

Abstract

The increasing importance of Veterinary Informatics is driving the implementation of integrated veterinary information management systems (VIMS) for the capture, storage, analysis and retrieval of animal data. In this paper, a decision tree algorithm was implemented, starting from the database of the University Veterinary Hospital at Federico II University of Naples, aiming at building a predictive model for an effective recognition of neoplastic diseases and zoonoses for cats and dogs focusing to Campania Region, in order to figure out, according to the One (Digital) Health perspective specifics, the connection between humans, animals, and surrounding environment.

Paper Citation

in Harvard Style

Tamburis O., Masciari E., Esposito C. and Fatone G. (2021). Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-521-0, pages 157-164. DOI: 10.5220/0010517201570164

in Bibtex Style

@conference{data21,
author={Oscar Tamburis and Elio Masciari and Christian Esposito and Gerardo Fatone},
title={Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2021},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010517201570164},
isbn={978-989-758-521-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital
SN - 978-989-758-521-0
AU - Tamburis O.
AU - Masciari E.
AU - Esposito C.
AU - Fatone G.
PY - 2021
SP - 157
EP - 164
DO - 10.5220/0010517201570164