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

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

Deep Learning, BiLSTM, Text classification, Word Embedding, Clinical Trials

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

Jasmir Jasmir1
, Willy Riyadi2
, Silvia Rianti Agustini3
, Yulia Arvita4
, Despita Meisak5
, Lies Aryani6

Universitas Dinamika Bangsa Jambi Indonesia

22-08-2022

Fajar bagus W

PDF

Indonesia

Text