A Machine Learning Approach for Drug Analysis
Rushikesh Gudade, Atharva Bongirwar, Pratik Jangam, Abhishek Giri, Dipali Himmatrao Patil · 2023
People nowadays depend on medicinal drugs in their day-to-day life. Some people at a very young age start taking pills and continue to take pills daily. Chronic back pain, birth control, migraine, acne treatment, weight gain, weight loss, headache, back pain, rashes, dizziness, etc., are the common reasons of pills intake. To get cured, people take pills without the help of a doctor. It works, but sometimes it doesn’t Due to these reasons, health will be affected in the later stages of life. To assist patients in selecting the best medication for their needs or, if none is available the best medication located nearby this study offers a machine-learning approach. The popularity of social media has just reached a new level, and important data is collected using sites like Twitter and reviews posted by users in various opinion groups. The process gets complicated to collect feedback from the patients who took pills and had either positive or negative si (k effects and after processing model gets integrated into a web application using different technologies. Technologies including Python frameworks like Flask, Django, and ReactJS are used to integrate the model into web applications. Supervised learning methods like Gradient Boosting, KNN (k nearest neighbor), and SVM to identify the most frequently used medication and the type of pain it is used to treat. The React framework is used to incorporate this machine learning model into a web application. The process known as gradient boosting yielded the most precise result. A user may upload a custom features dataset that includes reviews of various medications. This application will list the most common medications along with any side effects it may have and the age ranges in which they are most frequently used.