Redução do Viés Intra-Sujeito na Classificação da Doença de Parkinson pela Voz com Otimização por Colônia de Formigas
Resumo
A Doença de Parkinson (DP) é uma condição neurodegenerativa que afeta a voz. Estudos anteriores utilizaram aprendizado de máquina para diagnóstico de Parkinson a partir de dados obtidos pela voz. Entretanto, a grande maioria das abordagens existentes desconsideram a variação intrasujeito nas abordagens propostas. Este estudo propõe um método que aborda essa variação, além de empregar o algoritmo bioinspirado Otimização por Colônia de Formigas para seleção de características, reduzindo o sobreajuste. Foi obtida uma acurácia de 84,80% na distinção entre indivíduos com DP e controles saudáveis em uma abordagem mais justa e confiável, destacando o potencial de algoritmos bioinspirados no diagnóstico da DP.Referências
Braak, H. and Braak, E. (2000). Pathoanatomy of parkinson’s disease. J Neurol, 247 Suppl 2:II3–10.
da Silva, M. I., Felix, J. P., Prado, T. d. S., Chagas, A. L. d. B., Bucci, G. d. F. F. B., da Fonseca, A. U., and Soares, F. (2024). Sobre a análise de sinais de voz para o diagnóstico da doença de parkinson. Journal of Health Informatics, 16(Especial).
Daliri, M. R. (2012). Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement, 45(7):1729–1734.
Darwish, A. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2):231–246.
Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4):28–39.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1):29–41.
Felix, J., da Silva, M. I., Chagas, A. L., Salvini, R., Nascimento, H., and Soares, F. (2025). Analyzing the effect of replicated voice samples in Parkinson’s disease classification. In 2025 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pages 1–5, Vancouver, Canada. IEEE. To appear.
Govindu, A. and Palwe, S. (2023). Early detection of parkinson’s disease using machine learning. Procedia Computer Science, 218:249–261. International Conference on Machine Learning and Data Engineering.
Hayes, M. T. (2019). Parkinson’s disease and parkinsonism. The American Journal of Medicine, 132(7):802–807.
Ho, A., Iansek, R., Marigliani, C., Bradshaw, J., and Gates, S. (1998). Speech impairment in a large sample of patients with parkinson’s disease. Behavioural neurology, 11:131–137.
Little, M., Mcsharry, P., Hunter, E., Spielman, J., and Ramig, L. (2009). Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. IEEE transactions on bio-medical engineering, 56:1015.
Naranjo, L., Pérez, C. J., Campos-Roca, Y., and Martín, J. (2016). Addressing voice recording replications for parkinson’s disease detection. Expert Systems with Applications, 46:286–292.
Ouhmida, A., Terrada, O., Raihani, A., Cherradi, B., and Hamida, S. (2021). Voice-based deep learning medical diagnosis system for parkinson’s disease prediction. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN), pages 1–5.
Parlar, T. (2021). A heuristic approach with artificial neural network for parkinson’s disease. International Journal of Applied Mathematics Electronics and Computers, 9:1–6.
Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A.-E., and Lang, A. E. (2017). Parkinson disease. Nature Reviews Disease Primers, 3(1):1–21.
Prusty, S., Patnaik, S., and Dash, S. K. (2022). Skcv: Stratified k-fold cross-validation on ml classifiers for predicting cervical cancer. Frontiers in Nanotechnology, 4.
Rana, A., Dumka, A., Singh, R., Rashid, M., Ahmad, N., and Panda, M. (2022). An efficient machine learning approach for diagnosing parkinson’s disease by utilizing voice features. Electronics, 11:3782.
Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu, M., Aydin, T., Isenkul, M. E., and Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform. Applied Soft Computing, 74:255–263.
Solana-Lavalle, G., Galan-Hernandez, J., and Rosas-Romero, R. (2020). Automatic parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features. Biocybernetics and Biomedical Engineering, 40.
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., and Ramig, L. O. (2012). Novel speech signal processing algorithms for high-accuracy classification of parkinson’s disease. IEEE Trans Biomed Eng, 59(5):1264–1271.
Varghese, J., Brenner, A., Fujarski, M., van Alen, C. M., Plagwitz, L., and Warnecke, T. (2024). Machine learning in the parkinson’s disease smartwatch (pads) dataset. npj Parkinson’s Disease, 10(1):9.
da Silva, M. I., Felix, J. P., Prado, T. d. S., Chagas, A. L. d. B., Bucci, G. d. F. F. B., da Fonseca, A. U., and Soares, F. (2024). Sobre a análise de sinais de voz para o diagnóstico da doença de parkinson. Journal of Health Informatics, 16(Especial).
Daliri, M. R. (2012). Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement, 45(7):1729–1734.
Darwish, A. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2):231–246.
Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4):28–39.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1):29–41.
Felix, J., da Silva, M. I., Chagas, A. L., Salvini, R., Nascimento, H., and Soares, F. (2025). Analyzing the effect of replicated voice samples in Parkinson’s disease classification. In 2025 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pages 1–5, Vancouver, Canada. IEEE. To appear.
Govindu, A. and Palwe, S. (2023). Early detection of parkinson’s disease using machine learning. Procedia Computer Science, 218:249–261. International Conference on Machine Learning and Data Engineering.
Hayes, M. T. (2019). Parkinson’s disease and parkinsonism. The American Journal of Medicine, 132(7):802–807.
Ho, A., Iansek, R., Marigliani, C., Bradshaw, J., and Gates, S. (1998). Speech impairment in a large sample of patients with parkinson’s disease. Behavioural neurology, 11:131–137.
Little, M., Mcsharry, P., Hunter, E., Spielman, J., and Ramig, L. (2009). Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. IEEE transactions on bio-medical engineering, 56:1015.
Naranjo, L., Pérez, C. J., Campos-Roca, Y., and Martín, J. (2016). Addressing voice recording replications for parkinson’s disease detection. Expert Systems with Applications, 46:286–292.
Ouhmida, A., Terrada, O., Raihani, A., Cherradi, B., and Hamida, S. (2021). Voice-based deep learning medical diagnosis system for parkinson’s disease prediction. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN), pages 1–5.
Parlar, T. (2021). A heuristic approach with artificial neural network for parkinson’s disease. International Journal of Applied Mathematics Electronics and Computers, 9:1–6.
Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A.-E., and Lang, A. E. (2017). Parkinson disease. Nature Reviews Disease Primers, 3(1):1–21.
Prusty, S., Patnaik, S., and Dash, S. K. (2022). Skcv: Stratified k-fold cross-validation on ml classifiers for predicting cervical cancer. Frontiers in Nanotechnology, 4.
Rana, A., Dumka, A., Singh, R., Rashid, M., Ahmad, N., and Panda, M. (2022). An efficient machine learning approach for diagnosing parkinson’s disease by utilizing voice features. Electronics, 11:3782.
Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu, M., Aydin, T., Isenkul, M. E., and Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform. Applied Soft Computing, 74:255–263.
Solana-Lavalle, G., Galan-Hernandez, J., and Rosas-Romero, R. (2020). Automatic parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features. Biocybernetics and Biomedical Engineering, 40.
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., and Ramig, L. O. (2012). Novel speech signal processing algorithms for high-accuracy classification of parkinson’s disease. IEEE Trans Biomed Eng, 59(5):1264–1271.
Varghese, J., Brenner, A., Fujarski, M., van Alen, C. M., Plagwitz, L., and Warnecke, T. (2024). Machine learning in the parkinson’s disease smartwatch (pads) dataset. npj Parkinson’s Disease, 10(1):9.
Publicado
09/06/2025
Como Citar
LOBO, Pedro Lemes Sixel; FELIX, Juliana Paula; SALVINI, Rogerio; COELHO, Clarimar; SOARES, Fabrizzio.
Redução do Viés Intra-Sujeito na Classificação da Doença de Parkinson pela Voz com Otimização por Colônia de Formigas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 521-532.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7506.
