An Novel Adaptive Solution in Machine Learning Approaches for Mining Serendipitous Drug usage to Handle Imbalanced Data from Social Media Comparing with Adaboost Algorithm

Abstract

The aim is mining serendipitous drug usage to handle imbalanced data from social media. Two machine learning algorithms decision tree with the sample size=12 and adaboost algorithm with sample size=12. The decision tree classifier has shown more accuracy of (98. 20%) in handling the imbalance data when compared with adaboost algorithm accuracy (87. 00%). By using the G-power tool the pre-test calculated with a g-power value = 80% and threshold 0. 05% confidence interval of 95% mean and standard deviation. It is found that the decision tree classifier has more accuracy percentage when compared with the adaboost algorithm.