Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia

Daniel Mendoza-Vasquez, Stephany Salazar-Chavez, Willy Ugarte

2021

Abstract

In recent years, multiple applications of machine learning have been visualized to solve problems in different contexts, in which the health field stands out. That is why, based on what has been previously described, there is a wide interest in developing models based on machine learning for the creation of solutions that support medical assistance for disease such as pediatric cancer. Our work defines the proposal of a technological model based on machine learning which seeks to analyze the input medical data to obtain a predictive result, oriented to support the decision making of the specialist physician in relation to the diagnosis and treatment of pediatric leukemia. For the evaluation of the proposed model, a web validation system was developed that communicates with a service hosted on a cloud server which performs the predictive analysis of the inputs entered by the physician. As a result, an accuracy rate of 92.86% was obtained in the diagnosis of pediatric leukemia using the multiclass boosted decision tree classification algorithm.

Paper Citation

in Harvard Style

Mendoza-Vasquez D., Salazar-Chavez S. and Ugarte W. (2021). Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 346-353. DOI: 10.5220/0010684600003058

in Bibtex Style

@conference{webist21,
author={Daniel Mendoza-Vasquez and Stephany Salazar-Chavez and Willy Ugarte},
title={Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={346-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010684600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia
SN - 978-989-758-536-4
AU - Mendoza-Vasquez D.
AU - Salazar-Chavez S.
AU - Ugarte W.
PY - 2021
SP - 346
EP - 353
DO - 10.5220/0010684600003058