Drug Recommendation System

Authors

Mehattar Sajida, Bhanu Prasad M.C.

Abstract

Using device mastering, our mission proposes a disorder prediction system and drug advice gadget. For minor issues, users should go to the sanatorium in character for exam, which takes a variety of time, and speak to treatment calls are very hard for corporations. This problem can be solved by means of an application that predicts illnesses, to be able to provide appropriate pointers concerning a healthful lifestyle. Over the beyond decade, the use of sickness-particular predictive tools has expanded with fitness troubles because of the variety of diseases and restrained affected person medical exercise. Therefore, in this device, customers can input on the spot and accurate disorder prediction symptoms and also predict the severity of the ailment. In one channel, the facts entered is go-checked with the database. Additionally, if the symptom is new and its major end result is entered into the database, any other channel will provide the estimated severity of the disorder. A internet/Android user application has been developed for portability, customization and ease of remote get right of entry to locations that aren't effortlessly available via physicians. Usually customers do not recognize all the remedy strategies for a selected disorder, the program also hopes to offer recommendation on tablets and medicines for the stated disorder. Therefore, this arrangement enables in facilitating fitness care.

Keywords

Drugs, Sentiment Analysis, Machine Learning Algorithms

Drug Recommendation System. Mehattar Sajida, Bhanu Prasad M.C.. 2023. IJIRCT, Volume 9, Issue 5. Pages 1-7. https://www.ijirct.org/viewPaper.php?paperId=2309011