An Interpretable Classification Model for Identifying Individuals with Attention Defict Hyperactivity Disorder
Resumo
Attention Deficit Hyperactivity Disorder (ADHD) is a psychiatric condition that affects around 5% of children around the world. The primary attention procedure is traditionally based on analysis of ratings collected in questionnaires called psychometrics. This work aims to investigate interpretable classification models capable of not only accurately identifying individuals with ADHD, but also explain it, by providing the evidences that lead to the outcome. We compare the performance of Explainable Boosting Machine (EBM) with 3 other classical decision tree-based models and observed similar results, with the distinction of EBM being a more interpretable model. We also assess explanations quantitative and qualitatively, demonstrating how they may actually help psychiatrists in their practice.Referências
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Tamilarasan Ramasamy, D. J. J. Early risk detection of depression from social media posts using hierarchical attention networks. JOURNAL OF ALGEBRAIC STATISTICS 13 (1): 483–489, 2022.
Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA dermatology 155 (10):1135–1141, 2019.
Arnold, L. E., Hodgkins, P., Kahle, J., Madhoo, M., and Kewley, G. Long-term outcomes of adhd: academic achievement and performance. Journal of attention disorders 24 (1): 73–85, 2020.
Association, A. P. et al. DSM-5: Manual diagnóstico e estatístico de transtornos mentais. Artmed Editora, 2014.
Berrar, D. Cross-validation. Encyclopedia of Bioinformatics and Computational Biology vol. 1, pp. 542–545, 2018.
Brasil, H. H. and Bordin, I. A. Convergent validity of k-sads-pl by comparison with cbcl in a portuguese speaking outpatient population. BMC psychiatry 10 (1): 1–11, 2010.
Capik, C. and Gozum, S. Psychometric features of an assessment instrument with likert and dichotomous response formats. Public Health Nursing 32 (1): 81–86, 2015.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., et al. Xgboost: extreme gradient boosting. R package version 0.4-2 1 (4): 1–4, 2015.
Cortes, C. and Vapnik, V. Support-vector networks. Machine learning 20 (3): 273–297, 1995.
Cutler, A., Cutler, D. R., and Stevens, J. R. Random forests. In Ensemble machine learning. Springer, pp. 157–175, 2012.
da Silva Jandre, C. R. O uso de personas e aprendizado de máquina na análise do perfil de crianças e adolescentes com transtorno de déficit de atenção/hiperatividade, 2021.
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L. Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). IEEE, pp. 80–89, 2018.
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. Revisiting deep learning models for tabular data. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan (Eds.). Vol. 34. Curran Associates, Inc., pp. 18932–18943, 2021.
Itani, S., Rossignol, M., Lecron, F., and Fortemps, P. Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder. PloS one 14 (4): e0215720, 2019.
Lipton, Z. C. The doctor just won’t accept that! arXiv preprint arXiv:1711.08037 , 2017.
Lipton, Z. C. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16 (3): 31–57, 2018.
Loh, H. W., Ooi, C. P., Barua, P. D., Palmer, E. E., Molinari, F., and Acharya, U. R. Automated detection of adhd: Current trends and future perspective. Computers in Biology and Medicine vol. 146, pp. 105525, 2022.
Mattos, P., Rohde, L. A., and Polanczyk, G. V. Adhd is undertreated in brazil. Brazilian Journal of Psychiatry vol. 34, pp. 513–514, 2012.
Molnar, C. Interpretable machine learning. Lulu Press, Inc, 2020.
Nori, H., Jenkins, S., Koch, P., and Caruana, R. Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 , 2019.
Oliveira, D. M., Ribeiro, A. H., Pedrosa, J. A., Paixão, G. M., Ribeiro, A. L. P., and Meira, W. Explaining end-to-end ecg automated diagnosis using contextual features. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp. 204–219, 2020.
Ribeiro, M. T., Singh, S., and Guestrin, C. Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 , 2016.
Robnik-Šikonja, M. and Bohanec, M. Perturbation-based explanations of prediction models. In Human and machine learning. Springer, pp. 159–175, 2018.
Rosi, E., Grazioli, S., Crippa, A., Candelieri, A., Ceccarelli, S. B., Mauri, M., Manzoni, M., Mauri, V., Trabattoni, S., Molteni, M., et al. Towards telediagnostic procedures in child neuropsychiatry: addressing adhd diagnosis and autism symptoms through supervised machine learning, 2022.
Simms, L. J., Zelazny, K., Williams, T. F., and Bernstein, L. Does the number of response options matter? psychometric perspectives using personality questionnaire data. Psychological assessment 31 (4): 557, 2019.
Slobodin, O., Yahav, I., and Berger, I. A machine-based prediction model of adhd using cpt data. Frontiers in Human Neuroscience vol. 14, 2020.
Spearman, C. The proof and measurement of association between two things. The American Journal of Psychology 15 (1): 72–101, 1904.
Tamilarasan Ramasamy, D. J. J. Early risk detection of depression from social media posts using hierarchical attention networks. JOURNAL OF ALGEBRAIC STATISTICS 13 (1): 483–489, 2022.
Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA dermatology 155 (10):1135–1141, 2019.
Publicado
2022-11-28
Como Citar
VENTURA, N. et al.
An Interpretable Classification Model for Identifying Individuals with Attention Defict Hyperactivity Disorder.
Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), [S.l.], p. 66-73, nov. 2022.
ISSN 2763-8944.
Disponível em: <https://sol.sbc.org.br/index.php/kdmile/article/view/24970>. Acesso em: 14 maio 2024.
doi: https://doi.org/10.5753/kdmile.2022.227962.
Seção
Health Applications of Machine Learning
