A hybrid PSO/ACO algorithm for classification
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A hybrid PSO/ACO algorithm for discovering classification rules in data mining
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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We propose and evaluate the use of a Particle Swarm Optimization/Ant Colony Optimization (PSO/ACO) methodology for classification and rule discovery in the context of medication postmarketing surveillance or pharmacovigilance. Our study considers a large data set of diabetic patients on two widely used antidiabetic drugs (rosiglitazone and pioglitazone), and the risk of myocardial infarction as an adverse effect. The goal is to determine the presence of previously undetected causal relationships between therapeutics, patient characteristics, and adverse medication outcomes. Since the proposed approach is able to discover classification rules, the elicited knowledge may suggest new hypotheses regarding associations between risk factors and an adverse event. Our classification results show high accuracy. Furthermore, several medication-related rules were discovered and analyzed. The elicited rules support previous studies from the medical literature. Moreover, one of the studied antidiabetic drugs (rosiglitazone) was found to have a significant higher risk of an adverse event on diabetic, hypertensive patients, as compared to the other drug. This last finding suggests that pioglitazone may have a protective effect against myocardial infarction on diabetic, hypertensive patients.