Previsão de Desfechos Clínicos em Pacientes com Tuberculose Usando Redes Neurais Perceptron Multicamadas: Análise Interativa de Dados e Visualizações
Resumo
A tuberculose é uma das principais doenças infecciosas do mundo, e seu tratamento exige análise eficaz de dados. Este estudo usa Redes Neurais Perceptron Multicamadas (MLP) e Visualizações Interativas para prever desfechos clínicos em pacientes com tuberculose. O objetivo é melhorar a compreensão e a tomada de decisões clínicas por meio de análises preditivas e visualizações interativas. Aplicaram-se técnicas de normalização e balanceamento para treinar o modelo MLP. Ferramentas interativas foram empregadas para exibir distribuições de dados, métricas de desempenho e matrizes de confusão. Os resultados mostram que a combinação de MLP com visualizações interativas é eficaz para interpretar desfechos clínicos e auxiliar no planejamento de tratamentos.
Palavras-chave:
Tuberculose, MLP, ML, Visualizações Interativas
Referências
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Jannah, A. W. and Al Kindhi, B. (2024). Optimization of early detection of tuberculosis: Use of multilayer perceptron and extreme learning machine with clinical data. Jurnal Indonesia Sosial Teknologi, 5(5).
Kanesamoorthy, K. and Dissanayake, M. B. (2021). Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm. The International Journal of Mycobacteriology, 10(3):279–284.
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Motta, I., Boeree, M., Chesov, D., Dheda, K., Günther, G., Horsburgh Jr, C. R., Kherabi, Y., Lange, C., Lienhardt, C., McIlleron, H. M., et al. (2023). Recent advances in the treatment of tuberculosis. Clinical Microbiology and Infection.
Obeagu, E. and Obeagu, G. (2024). Understanding immune cell trafficking in tuberculosis-hiv coinfection: The role of l-selectin pathways. Elite Journal of Immunology, 2(2):43–59.
Orjuela-Cañón, A. D., Jutinico, A. L., Awad, C., Vergara, E., and Palencia, A. (2022). Machine learning in the loop for tuberculosis diagnosis support. Frontiers in Public Health, 10:876949.
Simarmata, T. S., Isnanto, R. R., and Triwiyatno, A. (2023). Detection of pulmonary tuberculosis using neural network with feature extraction of gray level run-length matrix method on lung x-ray images. In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 570–574. IEEE.
Talukder, M. A., Sharmin, S., Uddin, M. A., Islam, M. M., and Aryal, S. (2024). Mlstl-wsn: machine learning-based intrusion detection using smotetomek in wsns. International Journal of Information Security, 23(3):2139–2158.
Viboonsang, P. and Kosolsombat, S. (2024). Network intrusion detection system using machine learning and deep learning. In 2024 IEEE International Conference on Cybernetics and Innovations (ICCI), pages 1–6. IEEE.
de Almeida Rodrigues, M. G., Sampaio, V., Lynn, T., and Endo, P. T. A brazilian classified data set for prognosis of tuberculosis, between january 2001 and april 2020.
health Organisation, W. (2023). Report 20-23, volume t/malaria/.
Jannah, A. W. and Al Kindhi, B. (2024). Optimization of early detection of tuberculosis: Use of multilayer perceptron and extreme learning machine with clinical data. Jurnal Indonesia Sosial Teknologi, 5(5).
Kanesamoorthy, K. and Dissanayake, M. B. (2021). Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm. The International Journal of Mycobacteriology, 10(3):279–284.
Maadi, M., Akbarzadeh Khorshidi, H., and Aickelin, U. (2021). A review on human–ai interaction in machine learning and insights for medical applications. International journal of environmental research and public health, 18(4):2121.
Motta, I., Boeree, M., Chesov, D., Dheda, K., Günther, G., Horsburgh Jr, C. R., Kherabi, Y., Lange, C., Lienhardt, C., McIlleron, H. M., et al. (2023). Recent advances in the treatment of tuberculosis. Clinical Microbiology and Infection.
Obeagu, E. and Obeagu, G. (2024). Understanding immune cell trafficking in tuberculosis-hiv coinfection: The role of l-selectin pathways. Elite Journal of Immunology, 2(2):43–59.
Orjuela-Cañón, A. D., Jutinico, A. L., Awad, C., Vergara, E., and Palencia, A. (2022). Machine learning in the loop for tuberculosis diagnosis support. Frontiers in Public Health, 10:876949.
Simarmata, T. S., Isnanto, R. R., and Triwiyatno, A. (2023). Detection of pulmonary tuberculosis using neural network with feature extraction of gray level run-length matrix method on lung x-ray images. In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 570–574. IEEE.
Talukder, M. A., Sharmin, S., Uddin, M. A., Islam, M. M., and Aryal, S. (2024). Mlstl-wsn: machine learning-based intrusion detection using smotetomek in wsns. International Journal of Information Security, 23(3):2139–2158.
Viboonsang, P. and Kosolsombat, S. (2024). Network intrusion detection system using machine learning and deep learning. In 2024 IEEE International Conference on Cybernetics and Innovations (ICCI), pages 1–6. IEEE.
Publicado
09/10/2024
Como Citar
PEREIRA, Ronilson W. S.; SERUFFO, Marcos; FIGUEIREDO, Karla.
Previsão de Desfechos Clínicos em Pacientes com Tuberculose Usando Redes Neurais Perceptron Multicamadas: Análise Interativa de Dados e Visualizações. In: WORKSHOP INVESTIGAÇÕES EM INTERAÇÃO HUMANO-DADOS (WIDE), 3. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 29-38.
DOI: https://doi.org/10.5753/wide.2024.245491.