Improvements in adverse drug reaction prediction

Author:

Zhu Wanyu,Wang Yusen,Ma Yuzhou,Wang Peirui,Cui Tingyue

Abstract

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.

Publisher

IOP Publishing

Subject

Computer Science Applications,History,Education

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5. A review on machine learning approaches and trends in drug discovery;Paula;Computational and structural biotechnology journal,2021

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