Comparison of Logistic Regression, Naive Bayes and Random Forest Classifier Methods for Drug Review

Abstract

Machine Learning techniques are popularly used in a wide range of applications. However, it is not yet clear which classifier is best suited for which data. Moreover, the proposed work comparing how Nave Bayes, Random Forest and Logistics Regression differ from each other based on a given Drug review dataset. Drug review analysis has become very useful in present times as classifying medicines based on their effectiveness through analyzing online reviews from users can assist future consumers in collecting knowledge and making better decisions about a particular drug. Here, we are collected drug review dataset and processed for analysis. For analytical study, R Programming is used. This dataset provides patient reviews on specific drugs along with related conditions, and the reviews are analyzing by patient rating, which reflects overall patient satisfaction. The objective of this proposed research is to measure the effectiveness level of a particular drug. This paper is comparing classifiers by evaluating their classification accuracy; precision, recall, F1-score, and area under the ROC curve are compared in terms of performance factor