Prediction of Skin Tumor Invasiveness: A National Analysis Through Explainable Artificial Intelligence (XAI)
Resumo
In Brazil, skin tumors represents the type of neoplasm with the highest incidence rate among the population. Because of this, this study explores the invasiveness of this disease using computational techniques to understand how specific patient characteristics influence its progression. Through the analysis of data provided by the Cancer Hospital Registry (RHC) of the National Cancer Institute José Alencar Gomes da Silva (INCA), and with the aid of Artificial Intelligence (AI) algorithms explained by the SHapley Additive exPlanations (SHAP) approach, the study reveals that the invasiveness of skin cancer is affected in a significantly different way by the individual characteristics of patients compared to analyses based on more general attributes. These findings underline the importance of personalization in medicine, suggesting that a deeper understanding of individual characteristics can lead to more accurate diagnoses and more effective treatments. Furthermore, the research highlights the role of XAI in clarifying these relationships, pointing to the need for more refined approaches in prevention, treatment, and the formulation of public health policies aimed at combating skin tumors, despite limitations such as data imbalance encountered during the study.Referências
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Shapley, L. S. et al. (1953). A value for n-person games.
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Taghizadeh, E., Heydarheydari, S., Saberi, A., JafarpoorNesheli, S., and Rezaeijo, S. M. (2022). Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods. BMC bioinformatics, 23(1):1–9.
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BRAZIL’S MINISTRY OF HEALTH (2021). Câncer de pele. Accessed on: July 07, 2023.
BRAZIL’S MINISTRY OF HEALTH (2022). Enfrentamento do câncer. Acessado em: 07/07/2023.
Fidler, I. J. (2003). The pathogenesis of cancer metastasis: the’seed and soil’hypothesis revisited. Nature reviews cancer, 3(6):453–458.
Ghazal, T. M., Al Hamadi, H., Umar Nasir, M., Gollapalli, M., Zubair, M., Adnan Khan, M., Yeob Yeun, C., et al. (2022). Supervised machine learning empowered multifactorial genetic inheritance disorder prediction. Computational Intelligence and Neuroscience, 2022.
Lee, H.-C., Lin, T.-C., Chang, C.-C., Lu, Y.-C. A., Lee, C.-M., and Purevdorj, B. (2022). Clinical risk factor prediction for second primary skin cancer: a hospital-based cancer registry study. Applied Sciences, 12(24):12520.
Liu, W.-C., Li, M.-X., Qian, W.-X., Luo, Z.-W., Liao, W.-J., Liu, Z.-L., and Liu, J.M. (2021). Application of machine learning techniques to predict bone metastasis in patients with prostate cancer. Cancer Management and Research, pages 8723–8736.
Lopes, M. C., de Matos Amorim, M., Freitas, V. S., and Calumby, R. T. (2021). Survival prediction for oral cancer patients: A machine learning approach. In Anais do IX Symposium on Knowledge Discovery, Mining and Learning, pages 97–104. SBC.
Lundberg, S. and Lee, S. (2017). A unified approach to interpreting model predictions. arxiv: 170507874. Ar. Xiv.
Lundberg, S. M. et al. (2020). Shap: Shapley additive explanations. Accessed on: February 12, 2024.
Luo, Y., Tseng, H.-H., Cui, S., Wei, L., Ten Haken, R. K., and El Naqa, I. (2019). Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR— Open, 1(1):20190021.
Molnar, C. (2020). Interpretable machine learning. [link].
Santos, M., de Lima, F. C. d. S., Martins, L. F. L., Oliveira, J. F. P., de Almeida, L. M., and de Camargo Cancela, M. (2023). Estimativa de incidência de câncer no brasil, 2023-2025. Revista Brasileira de Cancerologia, 69(1).
Schwartz, M. R., Luo, L., and Berwick, M. (2019). Sex differences in melanoma. Current epidemiology reports, 6:112–118.
Shapley, L. S. et al. (1953). A value for n-person games.
Silva, W. S., Oliveira, V. T., Araújo, S. S., Vieira, D., and Castro, M. F. (2022). Explainability e auditability: interpretando e validando modelos de machine learning. Sociedade Brasileira de Computação.
Sorayaie Azar, A., Babaei Rikan, S., Naemi, A., Bagherzadeh Mohasefi, J., Pirnejad, H., Bagherzadeh Mohasefi, M., and Wiil, U. K. (2022). Application of machine learning techniques for predicting survival in ovarian cancer. BMC Medical Informatics and Decision Making, 22(1):345.
Subasi, A., Panigrahi, S. S., Patil, B. S., Canbaz, M. A., and Klén, R. (2022). Advanced pattern recognition tools for disease diagnosis. In 5G IoT and Edge Computing for Smart Healthcare, pages 195–229. Elsevier.
Taghizadeh, E., Heydarheydari, S., Saberi, A., JafarpoorNesheli, S., and Rezaeijo, S. M. (2022). Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods. BMC bioinformatics, 23(1):1–9.
Yu, J., Zhou, Y., Yang, Q., Liu, X., Huang, L., Yu, P., and Chu, S. (2021). Machine learning models for screening carotid atherosclerosis in asymptomatic adults. Scientific reports, 11(1):22236.
Publicado
25/06/2024
Como Citar
MATA, Marcus Augusto Padilha da; LEITÃO JÚNIOR, Plínio de Sá.
Prediction of Skin Tumor Invasiveness: A National Analysis Through Explainable Artificial Intelligence (XAI). In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 366-376.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2244.
