Prediction of Major Adverse Cardiovascular Events (MACCE) Following Percutaneous Coronary Intervention Using ANFIS-PSO Model

Abstract

Background: This study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent angioplasty.Method: This is a retrospective cohort study comprised a total of 220 patients (69 women and 151 men) who underwent coronary angioplasty in Ekbatan medical center in Hamadan city, Iran between March 2009 until March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The performance of ANFIS models for predicting MACCE was compared with ANFIS-PSO and logistic regression.Results: Ninety-six patients (43.6%) experienced the MACCE event after ten years of follow-up. In multivariate analysis based on logistic regression model, variables such as age (OR = 1.05), smoking (OR = 3.53), diabetes (OR = 2.17) and stent length (OR = 3.12) had a significant effect on MACCE occurrence. Comparing the prediction performance of the models showed that the ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE.Conclusion: The performance of ANFIS-PSO has a minimum error and maximum accuracy compared to other models in the prediction of MACCE. Application of this model is recommended for intelligent monitoring of these patients, the classification of high-risk patients and the allocation of necessary medical and health resources based on the needs of these patients.