Patient Text Feedback Based Optimized Deep Learned Model to Identify the Impact of Therapy
Jagriti1, Vikas Khullar2, Harjit Pal Singh3, Manju Bala4

1Jagriti, CSE*, CT Institute of Engineering, Management and Technology, Jalandhar, India.
2Vikas Khullar, CSE, CT Institute of Engineering, Management and Technology, Jalandhar, India.
3Dr. Harjit pal singh, ECE., CT Institute of Engineering, Management and Technology, Jalandhar, India.
4Dr. Manju Bala, CSE, Khalsa College of Engineering and Technology, Jalandhar. India.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1960-1967 | Volume-8 Issue-12, October 2019. | Retrieval Number: L29061081219/2019©BEIESP | DOI: 10.35940/ijitee.L2906.1081219
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Abstract: Text classification or Text mining is a very demanding field because the content created by the user in natural language is not easily understandable. It becomes very important to systematically identify and extract subjective information from user content so that it can be easily understandable. The whole process is done by assigning a particular class to text. In the field of opinion mining, most of the work has been done in common areas like restaurants, electronic goods, movie feedback, etc. and a lot of work needs to be done in the area of healthcare and medical. So, the proposed work has been carried over healthcare. The aim of this study is to classify the text feedback of patient using optimized deep learning model to identify the impact of therapy. In proposed method comparison of CNN with machine learning algorithms has been done, in which, CNN gave better results in terms of accuracy (99.98%), precision (0.981), recall (0.981), mean squared error (0.282). Further, we have implemented the CNN with N-gram technique and found that this method improved the results of CNN based on precision (0.999), recall (0.999), mean squared error (0.001), area under curve (0.998) but accuracy remained the same.
Keywords: Sentiment, Opinion, Health, Therapy, Text Classification, Deep learning.
Scope of the Article: Classification