Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document

  • Jasmir Jasmir Universitas Dinamika Bangsa
  • Willy Riyadi Universitas Dinamika Bangsa
  • Silvia Rianti Agustini Universitas Dinamika Bangsa
  • Yulia Arvita Universitas Dinamika Bangsa
  • Despita Meisak Universitas Dinamika Bangsa
  • Lies Aryani Universitas Dinamika Bangsa
Keywords: Deep Learning, BiLSTM, Text classification, Word Embedding, Clinical Trials

Abstract

In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4.  meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5.

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Published
2022-08-22
How to Cite
Jasmir, J., Riyadi, W., Agustini, S. R., Arvita, Y., Meisak, D., & Aryani, L. (2022). Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 505 - 510. https://doi.org/10.29207/resti.v6i4.4005
Section
Artikel Teknologi Informasi