Supervised Machine Learning For Recommendation Of Drugs And Sentiment Rating

Abstract

The various diseases attacking the
human body, such as the coronavirus and so on;
nowadays, due to the increase in infections, there
are no systems and medical experts so that
patients can take medicines at their own risk. Still,
they cause severe damage to the patient's body
and cause death. To solve that problem, the
author introduces the drug recommendation
system based on machine learning and sentiment
in this paper. They can take the name of the
disease from the patient, recommend the drug for
the given condition, and provide the
SENTIMENT based on the experience of earlier
users. If the rating is high for the predicted
disease, then patients recommend and trust the
drug. The TF-IDF (Term frequency-inverse
document frequency) algorithm is used to extract
features. We use different machine learning
algorithms to determine accuracies, such as the
SGD classifier, Multilayer perceptron classifier,
Nave Bayes, Ridge classifier, Linear SVC,
Logistic Regression, WORVEC, and BAG of
WORDS. These features extracted will be applied
to the different machine learning algorithms. We
use the TF-IDF feature extraction algorithm
among all the algorithms because it performs best.
The UCI machine learning website used a DRUG
REVIEW dataset to implement the project