Smart Recommendation System for Off-the Shelf Medicines - Sathappan, Ratna; Sai Indira, Tholu; Meenapriyadarsini, A (International Journal of Engineering and Technology, 2018)
Internet usage has been at an all-time high from 2000’s vintage years. The people who have access to the internet use it for numerous reasons such as social networking, marketing, promoting, enhancing businesses, consultancy, research, gaming and the list goes on. In the recent years, Review websites have flourished, where people share their opinion about a product, with an increase in response rate and reliability. Recommendations are made by mining data from review websites. Traditional Recommendation systems are limited as they only consider certain metrics, such as product purchase details, product category. Recommendation systems are yet to gain popularity in the medical field. These days most patients are unable to figure out the medication that works in healing them in the best way possible, hence they turn to review websites in order to obtain a second opinion on the prescribed medication. In this work, we have developed a smart recommendation system for off-the Shelf Medical Drugs using machine learning and data analytics based on patient feedback. The patient feedback is unstructured data which is processed using data analytic tools. After which machine learning is used to recommend the best fit and compare the drugs. In this work, we predict the impact of a drug/ medicine on the patient to whom the medication was prescribed, using data mining techniques. Firstly, we detect the user’s polarity (positive/ negative/neutral) based on the patient feedback for a certain drug using sentiment analysis and opinion mining following which we use machine learning algorithms to track sentiment variation and to make a recommendation based on user polarity