Comparing ECLAT and Decision Tree for Drug Therapy Recommendation Rules on Multi Label Clinical Data

Muh Rayhan Fahreza Rayhan; Harlinda; Herdianti Darwis; Roesman Ridwan Raja

doi:10.56705/ijodas.v7i2.410

Authors

Muh Rayhan Fahreza Rayhan Universitas Muslim Indonesia
Harlinda Universitas Muslim Indonesia
Herdianti Darwis Universitas Muslim Indonesia
Roesman Ridwan Raja Kyushu Institute of Technology

DOI:

https://doi.org/10.56705/ijodas.v7i2.410

Keywords:

ECLAT, Decision Tree, Association Rule Mining, Drug Recommendation, Clinical Decision Support

Abstract

Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images covering seven RIC categories was used, and three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation achieved 85.71% accuracy on the fixed 42-image test set. Most errors occurred between visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective approach for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.

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