Open AccessDrug Disease Association Prediction Using NLP & Machine Learning
Author(s) -
Suchith Reddy Vemula
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37537
Subject(s) - relevance (law) , sentiment analysis , product (mathematics) , social media , field (mathematics) , medical prescription , computer science , order (exchange) , data science , association rule learning , the internet , exploratory analysis , information retrieval , artificial intelligence , machine learning , medicine , world wide web , business , political science , geometry , mathematics , finance , pure mathematics , law , pharmacology
The relevance of analysing user-generated data on the internet has lately increased owing to the vast amount of information that can be obtained via proper study of such data. The majority of this information can be found on social media sites like Facebook, Twitter, and LinkedIn. Opinions and reviews on goods, movies, prescriptions, hotels, and other items are among the data accessible on such platforms. Companies are increasingly relying on data mining and analysis to get a better understanding of public opinion on a certain topic. There has been sufficient study on the use of sentiment analysis in many areas such as product reviews, movies, hotels, and so on. However, in the field of medicine, such techniques must be given more weight, since the US Food and Medication Administration has done multiple research on the consequences of adverse drug responses on patients. Pharmaceutical firms must study the impact of regularly used pharma products on patients in order to understand the good and bad impacts of medications on patients. The goal of this study is to use ML models to analyse the review of patient evaluations in order to evaluate if the opinions represented in the reviews are positive (or) negative. Keywords: Data Pre-Processing, Exploratory Data Analysis, Feature Extraction, Sentimental Classification, ML Algorithms, Prediction, Visualisation .