Patient text feedback based optimized deep learned model to identify the impact of therapy

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.