title: A Hybrid ANN-GA Model to Prediction of Bivariate Binary Responses: Application to Joint Prediction of Occurrence of Heart Block and Death in Patients with Myocardial Infarction.
abstract: In medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. Due to the limitations of usual statistical models, other methods such as Artificial Neural Network (ANN) and hybrid models could be used. In this paper, we propose a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to prediction the occurrence of heart block and death in myocardial infarction (MI) patients simultaneously.
  
  
  
  For fitting and comparing the models, 263 new patients with definite diagnosis of MI hospitalized in Cardiology Ward of Hajar Hospital, Shahrekord, Iran, from March, 2014 to March, 2016 were enrolled. Occurrence of heart block and death were employed as bivariate binary outcomes. Bivariate Logistic Regression (BLR), ANN and hybrid ANN-GA models were fitted to data. Prediction accuracy was used to compare the models. The codes were written in Matlab 2013a and Zelig package in R3.2.2.
  
  
  
  The prediction accuracy of BLR, ANN and hybrid ANN-GA models was obtained 77.7%, 83.69% and 93.85% for the training and 78.48%, 84.81% and 96.2% for the test data, respectively. In both training and test data set, hybrid ANN-GA model had better accuracy.
  
  
  
  ANN model could be a suitable alternative for modeling and predicting bivariate binary responses when the presuppositions of statistical models are not met in actual data. In addition, using optimization methods, such as hybrid ANN-GA model, could improve precision of ANN model.
authors.affiliation: Department of Biostatistics and Epidemiology, Faculty of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran.
authors.forename: Negin-Sadat
authors.initials: NS
authors.lastname: Mirian
authors.affiliation: Department of Biostatistics and Epidemiology, Faculty of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran. sedehi1356@gmail.com.
authors.forename: Morteza
authors.initials: M
authors.lastname: Sedehi
authors.affiliation: Department of Biostatistics and Epidemiology, Faculty of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran.
authors.forename: Soleiman
authors.initials: S
authors.lastname: Kheiri
authors.affiliation: Department of Biostatistics and Epidemiology, Faculty of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Ahmadi
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D055614:Genetic Phenomena; D006327:Heart Block / Q000150:complications*; D006760:Hospitalization; D006761:Hospitals; D006801:Humans; D007492:Iran; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000150:complications / Q000401:mortality*; D016571:Neural Networks, Computer; D012984:Software
keywords: Artificial Neural Network; Bivariate Logistic Regression; Genetic Algorithm; Heart Block; Myocardial Infarction