2022
Oliveira, C.; Sousa, R.; Peixoto, H.; Machado, J.
Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning Proceedings Article
Em: A., Fernandez A. Almeida A. Gonzalez-Briones (Ed.): pp. 222-231, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 18650929, (cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119).
Resumo | Links | BibTeX | Etiquetas: Cardiology; Classification (of information); Clinical research; Data mining; Decision trees; Diagnosis; Diseases; Health risks; Heart; Machine learning; Optimization, Causes of death; Condition; Data mining methods; Data-mining tools; Health records; Heart disease; Heart disease diagnosis; Machine-learning; Medical teams; Patient information, Decision support systems
@inproceedings{Oliveira2022222,
title = {Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning},
author = {C. Oliveira and R. Sousa and H. Peixoto and J. Machado},
editor = {Fernandez A. Almeida A. Gonzalez-Briones A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141686201&doi=10.1007%2f978-3-031-18697-4_18&partnerID=40&md5=5b5ddaa362353509a172400b23b54b71},
doi = {10.1007/978-3-031-18697-4_18},
issn = {18650929},
year = {2022},
date = {2022-01-01},
journal = {Communications in Computer and Information Science},
volume = {1678 CCIS},
pages = {222-231},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Despite technological and clinical improvements, heart disease remains one of the leading causes of death worldwide. A significant shift in the paradigm would be for medical teams to be able to accurately identify, at an early stage, whether a patient is at risk of developing or having heart disease, using data from their health records paired with Data Mining tools. As a result, the goal of this research is to determine whether a patient has a cardiac condition by using Data Mining methods and patient information to aid in the construction of a Clinical Decision Support System. With this purpose, we use the CRISP-DM technique to try to forecast the occurrence of cardiac disorders. The greatest results were obtained utilizing the Random Forest technique and the Percentage Split sampling method with a 66% training rate. Other approaches, such as Naïve Bayes, J48, and Sequential Minimal Optimization, also produced excellent results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119},
keywords = {Cardiology; Classification (of information); Clinical research; Data mining; Decision trees; Diagnosis; Diseases; Health risks; Heart; Machine learning; Optimization, Causes of death; Condition; Data mining methods; Data-mining tools; Health records; Heart disease; Heart disease diagnosis; Machine-learning; Medical teams; Patient information, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}