Abstract
DNN (Deep neural network) has emerged as one of the standard methods to create a classification model. The most common issue affecting DNN performance is the class-imbalanced distribution dataset. This research designed two workflows for generating synthetic dataset using SMOTE algorithm, SDS-1, and SDS-2 dataset. We further investigated the optimal DNN parameters that generate the best optimum performance over those datasets. We used Indian Liver Patient Dataset (ILPD) from the oldest source, UCI Machine Learning Repository, with a total of 583 records, consist of 416 positives and 167 negatives data. We measured the DNN performance using sensitivity and F-score metric following the nature of the medical domain that mainly focused on identifying a particular disease. The experiment results revealed that DNN model with the learning rate of 1E-1, TanH activation function, Xavier weighting, the epoch of 40, and the hidden layers of 10, delivered the best sensitivity and F-score value, 98.40% and 99.18%, respectively. The results suggested that the workflow for generating the class-balanced dataset will leverage the DNN performance.
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Acknowledgments
The authors wish to thank Universitas YARSI for funding this research (No. 183/INT/UM/WRII/UY/VIII/2016).
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Diana, N.E., Ahmad, A.B., Mahardika, Z.P. (2020). Investigating the Optimal Parameterization of Deep Neural Network and Synthetic Data Workflow for Imbalance Liver Disorder Dataset Classification. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_9
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DOI: https://doi.org/10.1007/978-3-030-36056-6_9
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