Objective
Myocardial Infarction (MI) is a major cause of death and disability worldwide and may be the first manifestation of coronary artery disease (CAD). The current study was carried out to predict the cholesterol level in patients with MI using data mining methods, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models.
Method
The data of 596 patients who had been diagnosed with myocardial infarction (MI) were studied in the present study. The retrospective data set including gender, age, weight, height, pulse, glucose, creatinine, triglyceride, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were employed for predicting the cholesterol level. Correlation based feature selection was applied. Multilayer perceptron (MLP) ANN and SVM with radial basis function (RBF) kernel were used for the prediction based on the selected predictors. The performance of the ANN and SVM models was evaluated on the basis of correlation coefficient and mean absolute error.
Method
The data of 596 patients who had been diagnosed with myocardial infarction (MI) were studied in the present study. The retrospective data set including gender, age, weight, height, pulse, glucose, creatinine, triglyceride, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were employed for predicting the cholesterol level. Correlation based feature selection was applied. Multilayer perceptron (MLP) ANN and SVM with radial basis function (RBF) kernel were used for the prediction based on the selected predictors. The performance of the ANN and SVM models was evaluated on the basis of correlation coefficient and mean absolute error.
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