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Authors: Ernie Baptista ; Franco Vigil and Willy Ugarte

Affiliation: Universidad Peruana de Ciencias Aplicadas, Lima, Peru

Keyword(s): Random Forest, Machine Learning, Leishmaniasis, NTDs, Outbreaks.

Abstract: Leishmaniasis is part of a group of diseases called Neglected Tropical Diseases (NTDs) that affects poor and forgotten communities and reports more than 5,000 cases in regions like Brazil, Peru, and Colombia being categorized as endemic in these. In this study, we present a machine-learning model (Random Forest) to predict cases in the future and predict possible outbreaks using meteorological and epidemiological data of the province of la Convencion (Cusco - Peru). Understanding how climate variables affect leishmaniasis outbreaks is an important problem to help people to perform prevention systems. We used several techniques to obtain better metrics and improve our model performance such as synthetic data and hyperparameter optimization. Results showed two important climate factors to analyze and no outbreaks.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Baptista, E.; Vigil, F. and Ugarte, W. (2024). A Regression Based Approach for Leishmaniasis Outbreak Detection. In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE; ISBN 978-989-758-700-9; ISSN 2184-4984, SciTePress, pages 204-211. DOI: 10.5220/0012683900003699

@conference{ict4awe24,
author={Ernie Baptista. and Franco Vigil. and Willy Ugarte.},
title={A Regression Based Approach for Leishmaniasis Outbreak Detection},
booktitle={Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
year={2024},
pages={204-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012683900003699},
isbn={978-989-758-700-9},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE
TI - A Regression Based Approach for Leishmaniasis Outbreak Detection
SN - 978-989-758-700-9
IS - 2184-4984
AU - Baptista, E.
AU - Vigil, F.
AU - Ugarte, W.
PY - 2024
SP - 204
EP - 211
DO - 10.5220/0012683900003699
PB - SciTePress