Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89431
Título: | Improving the effectiveness of heart disease diagnosis with Machine Learning |
Autor(es): | Oliveira, Catarina Sousa, Regina Peixoto, Hugo Machado, José Manuel |
Palavras-chave: | Classification Data mining Decision support systems Heart disease Machine learning |
Data: | 2022 |
Editora: | Springer, Cham |
Revista: | Communications in Computer and Information Science |
Citação: | Oliveira, C., Sousa, R., Peixoto, H., Machado, J. (2022). Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_18 |
Resumo(s): | 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. |
Tipo: | Artigo em ata de conferência |
Descrição: | First Online: 13 October 2022 |
URI: | https://hdl.handle.net/1822/89431 |
ISBN: | 9783031186967 |
DOI: | 10.1007/978-3-031-18697-4_18 |
ISSN: | 1865-0929 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-18697-4_18 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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paams_cameraready.pdf | 342,93 kB | Adobe PDF | Ver/Abrir |