an efficient predictive model for myocardial infarction using cost-sensitive j48 model

background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized hospital, khorramabad, iran, in 2015. then, a hybrid feature selection method included weight by relief and genetic algorithm applied on the dataset to select the best features. after selection of the features, the metacost classifier applied on the sampled dataset. metacost made a cost sensitive j48 model by assigning different costs ratios for misclassified cases; include 1:10, 1:50, 1:100, 1:150 and 1:200. results: after applying the model on the imbalanced dataset, the cost ratio 1:200 led to the best results in comparison to not using feature selection and cost sensitive model. the model achieved sensitivity, f-measure and accuracy of 86.67%, 80% and 82.67%, respectively. conclusion: experiments on the real dataset showed that using the cost-sensitive method along with the hybrid feature selection method improved model performance. therefore, the model considered a reliable myocardial infarction prediction model.