Predictive Analysis of Adverse Drug effects using Machine Learning

Abstract: The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using databases like SIDER (Side Effect Research) and OFFSIDES (Government medical records). In this paper, three Machine Learning algorithms SVM (Support Vector Machine), Random Forest, and Gradient boosted trees are implemented on the datasets to predict various disorders caused due to adverse effects, and the performance is evaluated based on performance metrics such as average precision, recall, accuracy, f1 binary, f1 macro, and f1 micro. Finally, the two datasets were merged to understand and compare the performance of a combined dataset to a single dataset.