PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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.

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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