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Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
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Research article

Development of Artificial Neural Network Model for Medical Specialty Recommendation

Winda Hasuki, David Agustriawan, Arli Aditya Parikesit, Muammar Sadrawi, Moch Firmansyah, Andreas Whisnu, Jacqulin Natasya, Ryan Mathew, Florensia Irena Napitupulu and Nanda Rizqia Pradana Ratnasari

Volume 31, Issue 6 October 2023 Published 12 October 2023
https://doi.org/10.47836/pjst.31.6.05
KeywordsMachine learning, medical specialty, multilayer perceptron, neural network, recommendation
Article content

Abstract

Timely diagnosis is crucial for a patient’s future care and treatment. However, inadequate medical service or a global pandemic can limit physical contact between patients and healthcare providers. Combining the available healthcare data and artificial intelligence methods might offer solutions that can support both patients and healthcare providers. This study developed one of the artificial intelligence methods, artificial neural network (ANN), the multilayer perceptron (MLP), for medical specialist recommendation systems. The input of the system is symptoms and comorbidities. Meanwhile, the output is the medical specialist. Leave one out cross-validation technique was used. As a result, this study’s F1 score of the model was about 0.84. In conclusion, the ANN system can be an alternative to the medical specialist recommendation system.
Supporting literature

References

  1. Agrawal, A., Viktor, H. L., & Paquet, E. (2015, November 12-14). SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling. [Paper presentation]. International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), Lisbon, Portugal. https://doi.org/10.5220/0005595502260234
  2. Aguiar, F. S., Torres, R. C., Pinto, J. V. F., Kritski, A. L., Seixas, J. M., & Mello, F. C. Q. (2016). Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil. Medical & Biological Engineering & Computing, 54(11), 1751-1759. https://doi.org/10.1007/s11517-016-1465-1
  3. Alejo, R., Antonio, J. A., Valdovinos, R. M., & Pacheco-Sánchez, J. H. (2013). Assessments Metrics for Multi-class Imbalance Learning: A Preliminary Study. In J. A. Carrasco-Ochoa, J. F. Martinex-Trinidad, J. S. Rodriuez & G. S. D. Baja (Eds.), Pattern Recognition: 5th Mexican Conference, MCPR 2013, Querétaro, Mexico Proceedings 5 (pp. 335-343). Springer. https://doi.org/10.1007/978-3-642-38989-4_34
  4. Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47-58. https://doi.org/10.2478/v10136-012-0031-x
  5. Borghi, P. H., Zakordonets, O., & Teixeira, J. P. (2021). A COVID-19 time series forecasting model based on MLP ANN. Procedia Computer Science, 181, 940-947. https://doi.org/10.1016/j.procs.2021.01.250
  6. Buscema, P. M., Gitto, L., Russo, S., Marcellusi, A., Fiori, F., Maurelli, G., Massini, G., & Mennini, F. S. (2017). The perception of corruption in health: AutoCM methods for an international comparison. Quality & Quantity, 51(1), 459-477. https://doi.org/10.1007/s11135-016-0315-4
  7. Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018). Deep learning and its applications in biomedicine. Genomics, Proteomics & Bioinformatics, 16(1), 17-32. https://doi.org/10.1016/j.gpb.2017.07.003
  8. Casagranda, I., Costantino, G., Falavigna, G., Furlan, R., & Ippoliti, R. (2016). Artificial neural networks and risk stratification models in emergency departments: The policy maker’s perspective. Health Policy, 120(1), 111-119. https://doi.org/10.1016/j.healthpol.2015.12.003
  9. Chai, S. S., Cheah, W. L., Goh, K. L., Chang, Y. H. R., Sim, K. Y., & Chin, K. O. (2021). A multilayer perceptron neural network model to classify hypertension in adolescents using anthropometric measurements: A cross-sectional study in Sarawak, Malaysia. Computational and Mathematical Methods in Medicine, 2021, Article 2794888. https://doi.org/10.1155/2021/2794888
  10. da Silva, I. N., Spatti, D. H., Flauzino, R. A., Liboni, L. H. B., & Alves, S. F. D. R. (2017). Artificial neural network architectures and training processes. In Artificial Neural Networks: A Practical Course (pp. 21-28). Springer International Publishing. https://doi.org/10.1007/978-3-319-43162-8_2
  11. Feng, S., Zhou, H., & Dong, H. (2019). Using deep neural network with small dataset to predict material defects. Materials & Design, 162, 300-310. https://doi.org/10.1016/j.matdes.2018.11.060
  12. Ippoliti, R., Falavigna, G., Zanelli, C., Bellini, R., & Numico, G. (2021). Neural networks and hospital length of stay: An application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation, 19(1), Article 67. https://doi.org/10.1186/s12962-021-00322-3
  13. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101
  14. Koziarski, M., Woźniak, M., & Krawczyk, B. (2020). Combined cleaning and resampling algorithm for multi-class imbalanced data with label noise. Knowledge-Based Systems, 204, Article 106223. https://doi.org/10.1016/j.knosys.2020.106223
  15. Kulkarni, A., Chong, D., & Batarseh, F. A. (2021). Foundations of data imbalance and solutions for a data democracy. In F. A. Batarseh & R. Yang (Eds.), Data Democracy (pp. 83-106). Academic Press. https://doi.org/10.1016/B978-0-12-818366-3.00005-8
  16. Kumar, A., Prakash, U. M., & Sharma, G. K. (2021). Disease prediction and doctor recommendation system using machine learning approaches. International Journal for Research in Applied Science and Engineering Technology, 9(VII), 34-44. https://doi.org/10.22214/ijraset.2021.36234
  17. Lee, H., Kang, J., & Yeo, J. (2021). Medical specialty recommendations by an artificial intelligence chatbot on a smartphone: Development and deployment. Journal of Medical Internet Research, 23(5), Article e27460. https://doi.org/10.2196/27460
  18. Luque, A., Carrasco, A., Martín, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216-231. https://doi.org/10.1016/j.patcog.2019.02.023
  19. Olson, M., Wyner, A., & Berk, R. (2018, December 2-8). Modern neural networks generalize on small data sets. [Paper presentation]. Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada.
  20. Pasini, A. (2015). Artificial neural networks for small dataset analysis. Journal of Thoracic Disease, 7(5), 953-960. https://doi.org/10.3978/j.issn.2072-1439.2015.04.61
  21. Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In L. Liu & M. T. Ozsu (Eds.), Encyclopedia of Database Systems (pp. 532-538). Springer. https://doi.org/10.1007/978-0-387-39940-9_565
  22. Rémy, N. M., Martial, T. T., & Clémentin, T. D. (2018). The prediction of good physicians for prospective diagnosis using data mining. Informatics in Medicine Unlocked, 12, 120-127. https://doi.org/10.1016/j.imu.2018.07.005
  23. Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS One, 14(2), Article e0212356. https://doi.org/10.1371/journal.pone.0212356
  24. Silitonga, P., Bustamam, A., Muradi, H., Mangunwardoyo, W., & Dewi, B. E. (2021). Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset. Applied Sciences, 11(3), Article 943. https://doi.org/10.3390/app11030943
  25. So, B., & Valdez, E. A. (2021). The SAMME.C2 algorithm for severely imbalanced multi-class classification. ArXiv. https://doi.org/10.48550/arXiv.2112.14868
  26. Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020). Boosting methods for multi-class imbalanced data classification: an experimental review. Journal of Big Data, 7(1), Article 70. https://doi.org/10.1186/s40537-020-00349-y
  27. Webb, G. I., Sammut, C., Perlich, C., Horváth, T., Wrobel, S., Korb, K. B., Noble, W. S., Leslie, C., Lagoudakis, M. G., Quadrianto, N., Buntine, W. L., Quadrianto, N., Buntine, W. L., Getoor, L., Namata, G., Getoor, L., Han, X. J. J., Ting, J. A., Vijayakumar, S., … & Raedt, L. D. (2011). Leave-One-Out Cross-Validation. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 600-601). Springer. https://doi.org/10.1007/978-0-387-30164-8_469
  28. Yao, L., Zhong, Y., Wu, J., Zhang, G., Chen, L., Guan, P., Huang, D., & Liu, L. (2019). Multivariable logistic regression and back propagation artificial neural network to predict diabetic retinopathy. Diabetes, Metabolic Syndrome and Obesity, 12, 1943-1951. https://doi.org/10.2147/DMSO.S219842
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