Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms

TL;DR

This paper presents a genetic algorithm-based approach to optimize a multiple classifier system for side effect discovery, outperforming single classifiers in accuracy and efficiency.

Contribution

It introduces a novel framework that tunes multiple classifiers using genetic algorithms for improved side effect detection.

Findings

Higher partial AUC than single classifiers

Efficient detection of side effects

Low false positive rate

Abstract

In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate.