Classification of health care text using NLP and ML
Article No.: 73, Pages 1 - 7
Abstract
Now Health care industry grows so much and patient share their experience and those reviews help other to enhance and improving their knowledge. The trend of sharing thoughts, ideas, opinions, reviews, ratings, etc. on social media is growing, which gave lot of unstructured data. For these types of unstructured data supervised learning methods are good to extract something and improving the performance of the machine. NLP is the best method to extract information from the text data. Not only NLP but Machine Learning algorithm with NLP methods gave which is high accurate to train our machine for better prediction. In this paper UCI ML drug review dataset used from kaggle website and this dataset provide patients reviews on specified drugs along with some conditions. Bag of words and TF-IDF model along with Naïve bayes and Passive aggressive classifier algorithm to train and test the machine to classify the patients review text data. Our objective is to find which NLP model and ML algorithm is good based on the accuracy and is our machine able to classify patient review and predict condition based on the review.
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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