The Efficiency of Machine Learning Algorithms in the Prediction of Drug Reactions in Clinical Settings
Christopher Agbonkhese, Hettie Abimbola Soriyan, Kolawole Samuel Mosaku · Indian Journal of Computer Science and Engineering · 2023
In the rapidly evolving landscape of healthcare, the efficient detection of drug reactions is of paramount importance to ensure patient safety and optimize treatment outcomes.This article presents a comprehensive study on the application of machine learning techniques for the early detection of drug reactions through the analysis of drug prescriptions in clinical settings.The study utilized a formulated model with classification and regression tree algorithm, iterative dichotomizer 3, gaussioan, naïve bayes, Bernoulli naïve bayes, multinomial naïve bayes, with adaptive boosting algorithm to extract valuable insights from health records and prescription data and predict the possible occurrence of adverse reactions from prescribed medications.A comparative analysis of the efficiencies of the various algorithms was carried out based on the computational learning theory.Among the myriad models scrutinized, the results showed that an ensemble comprising ID3, MultinomialNB, and AdaBoost emerged as a standout performer, consistently showcasing exceptional performance across multiple metrics.