Abordagem iCAD: Explorando Inferência Neuro-Fuzzy na Predição da Doença da Artéria Coronária no Cenário da IoT
Resumo
As doenças cardiovasculares, especialmente a doença da artéria coronária (CAD), representam um grande desafio para a saúde global. As propostas que vem ganhando reconhecimento na literatura na predição da CAD, são os sistemas de inferência Neuro-Fuzzy. Considerando este cenário, este artigo discute a concepção de uma abordagem, denominada iCAD, que explora uma arquitetura distribuída na IoT e faz uso de ANFIS para auxiliar na predição da CAD. A abordagem concebida foi avaliada a partir das suas funcionalidades e alertas gerados, além das métricas relacionadas à predição. Neste sentido, a abordagem iCAD obteve 92,28% de Acurácia, 92,39% de Precisão, 93,75% de Especificidade, 92,28% de Sensibilidade e 92,29% de F1-Score.Referências
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de Souza, R. S., Barbará Lopes, J. L., Resin Geyer, C. F., da Rosa Silveira João, L., Afonso Cardozo, A., Corrêa Yamin, A., Gadotti, G. I., and Victoria Barbosa, J. L. (2019). Continuous monitoring seed testing equipaments using internet of things. Computers and Electronics in Agriculture, 158:122 – 132.
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Khan, M. A. and Algarni, F. (2020). A healthcare monitoring system for the diagnosis of heart disease in the iomt cloud environment using msso-anfis. IEEE Access, 8:122259–122269.
Manogaran, G., Varatharajan, R., and Priyan, M. K. (2018). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications, 77(4):4379–4399.
Patidar, A. and Suman, U. (2015). A survey on software architecture evaluation methods. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pages 967–972. IEEE.
Perera, C. (2017). Sensing as a Service for Internet of Things: A Roadmap. Leanpub Publishers. Shanmugapriya, P. and Suresh, R. (2012). Software architecture evaluation methods-a survey. International Journal of Computer Applications, 49(16).
Silva, M., Cardoso, M. A., Machado, M. C., and Ferreira, A. P. L. (2019). Sistema de inferência fuzzy para estimativa de crescimento populacional. Anais do Salão Internacional de Ensino, Pesquisa e Extensão, 11(2).
Sonal, Reddy, S., and Kumar, D. (2022). Early congenital heart defect diagnosis in neonates using novel wban based three-tier network architecture. Journal of King Saud University Computer and Information Sciences, 34(6, Part B):3661–3672.
Ziasabounchi, N. and Askerzade, I. (2014). Anfİs based classification model for heart disease prediction. International Journal of Engineering & Computer Science IJECS-IJENS, 14:146402–7373.
Alves, C. (2010). Cardiologia do Exercício: do Atleta Ao Cardiopata. Baihaqi, W. M., Setiawan, N. A., and Ardiyanto, I. (2016). Rule extraction for fuzzy expert system to diagnose coronary artery disease. pages 136–141.
de Souza, R. S., Barbará Lopes, J. L., Resin Geyer, C. F., da Rosa Silveira João, L., Afonso Cardozo, A., Corrêa Yamin, A., Gadotti, G. I., and Victoria Barbosa, J. L. (2019). Continuous monitoring seed testing equipaments using internet of things. Computers and Electronics in Agriculture, 158:122 – 132.
Jang, J.-S. R. (1993). Anfis adaptive-network-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics, 23(3):665–685.
Khan, M. A. and Algarni, F. (2020). A healthcare monitoring system for the diagnosis of heart disease in the iomt cloud environment using msso-anfis. IEEE Access, 8:122259–122269.
Manogaran, G., Varatharajan, R., and Priyan, M. K. (2018). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications, 77(4):4379–4399.
Patidar, A. and Suman, U. (2015). A survey on software architecture evaluation methods. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pages 967–972. IEEE.
Perera, C. (2017). Sensing as a Service for Internet of Things: A Roadmap. Leanpub Publishers. Shanmugapriya, P. and Suresh, R. (2012). Software architecture evaluation methods-a survey. International Journal of Computer Applications, 49(16).
Silva, M., Cardoso, M. A., Machado, M. C., and Ferreira, A. P. L. (2019). Sistema de inferência fuzzy para estimativa de crescimento populacional. Anais do Salão Internacional de Ensino, Pesquisa e Extensão, 11(2).
Sonal, Reddy, S., and Kumar, D. (2022). Early congenital heart defect diagnosis in neonates using novel wban based three-tier network architecture. Journal of King Saud University Computer and Information Sciences, 34(6, Part B):3661–3672.
Ziasabounchi, N. and Askerzade, I. (2014). Anfİs based classification model for heart disease prediction. International Journal of Engineering & Computer Science IJECS-IJENS, 14:146402–7373.
Publicado
21/07/2024
Como Citar
LAMBRECHT, Rodrigo; ALBANDES, Rogério; DILLI, Renato; OLIVEIRA, Lizandro de Souza; REISER, Renata; YAMIN, Adenauer.
Abordagem iCAD: Explorando Inferência Neuro-Fuzzy na Predição da Doença da Artéria Coronária no Cenário da IoT. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 16. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 81-90.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2024.2586.
