Pharma Guide: Medicine Suggestions using ML

Abstract

The contemporary world is so fast that healthcare professionals are not always readily available and, in this case, the application of machine learning as a method of rapid diagnosis and prescribing treatment can be of great importance in resolving this issue. Covered in the project is a Medicine Suggestions which is a machine learning algorithm that can predict diseases given the symptoms the user has inputted and can offer the correct recommendations of what medication, home remedies and other disease management practices should be used. With the help of the established classifiers, including Support Vector Machine (SVC), Random Forest, and K-Nearest Neighbors (KNN), the system will be able to identify the disease accurately and offer a holistic treatment plan, including medications, diet, exercises, and precautions. It is not only a smart application that will help in the diagnosis but will also offer a comprehensive method in managing health by proposing preventive care, drugs, and household remedies thereby maximizing the overall healthcare experience of the user. Of all the named SVC, KNN, Gradient Boosting, multinomial NB, and Random Forest classifiers were used. The SVC classifier is the most accurate.