A Comparative Study on Aspects Level Drug Reviews using Back Propagation Neural Networks

Several aspects, such as drug interactions and adverse effects, must be taken into account before prescribing a drug. The fact that some pharmacological qualities, such as side effects, are dependent on patient variables such as age and gender, complicates the procedure even further. Our goal is to create a platform that will aid doctors in prescription medications. This paper devises a method for searching for medications that meet a set of criteria based on drug attributes. Both healthcare professionals who prescribe and dispense pharmaceuticals, as well as drug users, should take several precautions when using pharmaceutical drugs. Prescription drug interactions, interactions with the patient’s existing medicine, potential adverse effects, and contraindications must all be considered. Furthermore, patient characteristics such as age, gender, habits, and genetic profiles influence pharmacological attributes such as side effects and efficacy. The objective is to develop a system that will assist medical professionals and drug users in choosing and locating appropriate drugs. Users can select side effects and the approach answers to their needs. Data about drugs comes from a variety of places. Medicine data, on the other hand, is typically noisy and incomplete because it is either manually developed or mechanically retrieved from text resources like drug labels. Data science technology known as a supervised Machine learning algorithm can be used to analyze medications based on various aspects with better accuracy to deal with incomplete and noisy data.