Improvements in adverse drug reaction prediction

Abstract

This report investigates prediction on adverse drug reactions (ADR) with kernel and imbalance data mechanisms. The hypothesis is that different types of kernel lead to different prediction results, which suggests deciding the best-fit kernel might be a critical way of improving prediction accuracy. Besides, it was also hypothesized that edge cases in real-life setting would cause imbalance in the dataset, thus further causing inaccuracy in prediction. Similarly, attempting to add class weight to various machine learning models could also be a way to improve prediction accuracy. Hence, these hypotheses are being explored in this study.