Open AccessAn Effective Stratified K-Fold Algorithm with Logistic Regression for Drug Feedback Data
Author(s) -
D. Swathi,
U Kumaran
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8166.038620
Subject(s) - logistic regression , random forest , naive bayes classifier , computer science , machine learning , support vector machine , algorithm , artificial intelligence , drug , data mining , medicine , pharmacology
Drug reviews are commonly used in pharmaceutical industry to improve the medications given to patients. Generally, drug review contains details of drug name, usage, ratings and comments by the patients. However, these reviews are not clean, and there is a need to improve the cleanness of the review so that they can be benefited for both pharmacists and patients. To do this, we propose a new approach that includes different steps. First, we add extra parameters in the review data by applying VADER sentimental analysis to clean the review data. Then, we apply different machine learning algorithms, namely linear SVC, logistic regression, SVM, random forest, and Naive Bayes on the drug review specify dataset names. However, we found that the accuracy of these algorithms for these datasets is limited. To improve this, we apply stratified K-fold algorithm in combination with Logistic regression. With this approach, the accuracy is increased to 96%.