Pre-screening Textual Based Evaluation for the Diagnosed Female Breast Cancer (WBC)

Pre-screening Textual Based Evaluation for the Diagnosed Female Breast Cancer (WBC)

Mahmood Alhlffee 

Department of DIEC, IIIE, Universidad Nacional Del Sur, Bahía Blanca 8000, Argentina

Corresponding Author Email: 
mahmood@uns.edu.ar
Page: 
255-263
|
DOI: 
https://doi.org/10.18280/ria.330401
Received: 
15 April 2019
|
Accepted: 
27 June 2019
|
Published: 
30 October 2019
| Citation

OPEN ACCESS

Abstract: 

The existing virtual assistants (VAs) for medical services cannot output satisfactory results on Chinese language processing (CLP). This paper attempts to design a VA that identifies the seriousness and improves the awareness of breast cancer (BC) based on inputs of Chinese texts. Our VA was developed based on the neural network called long short-term memory (LSTM), integrating two N-gram models, namely, bigram and trigram. The integrated models are critical to text-based Chinese word segmentation (CWS). The sequence-to-sequence learning was introduced to covert the CWS into a framework of sequence classification. The proposed VA was compared with several state-of-the-art methods through an experiment. The results show that our method achieved a high accuracy (94%~97%) in identifying the high-frequency characters. The research findings are helpful to the BC identification of Chinese women.

Keywords: 

virtual assistance, sequence to sequence neural network, bigram and trigram

1. Introduction
2. Related Frame-Work
3. VA Design for Text-Based Pattern Matching Schemes
4. System Architecture
5. Model Evaluated Result
6. Conclusion