Prediction of cholesterol level in patients with myocardial infarction based on medical data mining methods
Keywords:
Artificial neural networks (ANNs), cholesterol level, medical data mining, myocardial infarction (MI), support vector machine (SVM).Abstract
Myocardial infarction (MI) is a significant reason for death and disability over the world and might be the first signof coronary artery disease. The current study was carried out to predict the cholesterol level in patients with MI usingdata mining methods, artificial neural networks (ANNs) and support vector machine (SVM) models. The data of 596patients, who had been diagnosed with segment elevation MI were analysed in the present study. The retrospectivedataset including gender, age, weight, height, pulse, glucose, creatinine, triglyceride, high-density lipoprotein, andlow-density lipoprotein was used for predicting the cholesterol level. Correlation based feature selection was applied.Multilayer perceptron (MLP) ANNs and SVM with radial basis function kernel were used for the prediction basedon the selected predictors. The performance of the ANNs and SVM models was evaluated on the basis of correlationcoefficient and mean absolute error. The estimated correlation coefficients observed and predicted values were 0.94 forANNs and 0.88 for SVM in training dataset (n=376), and 0.95 for ANNs and 0.90 for SVM in testing dataset (n=160),respectively. ANNs and SVM models yielded mean absolute error of 7.37 and 14.18 in training dataset, and 7.87 and14.71 in testing dataset, consecutively. The results of the performance evaluation showed that MLP ANNs performedbetter for the prediction of cholesterol level in patients with MI in comparison to SVM. The proposed MLP ANNs modelmight be employed for predicting the level of cholesterol for MI patients in clinical decision support process.
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