1H NMR spectroscopy was used for the diagnosis of coronary heart disease (CHD) by using human blood plasma samples. One-dimensional 1H NMR spectra from 29 normal and 35 CHD patients were obtained and investigated. Classification model was built on the basis of linear discriminant analysis in order to establish adequate model or discrimination between pathological and normal samples. Because of high similarity between 1H NMR spectra of healthy samples and patients, a feature-selection method can be used to reduce complexity of the model and improve the classification performance of the built classifier. In this paper, we presented a genetic algorithm (GA) based feature-selection method to find informative features that play a significant role in discrimination of samples. Selected subsets from multiple GA runs were used to build a classifier. The most informative features were selected according to classification performance of classifier for training and internal test set samples. The results of analysis showed that our approach can be used to improve discriminating power of classification model and simultaneously identify the important features for the diagnosis purpose and can be used in the diagnosis of CHD in patients without employing any angiographic technique.
کلید واژگان :coronary heart disease, 1H NMR spectroscopy, disease diagnosis, feature selection, genetic algorithm
ارزش ریالی : 600000 ریال
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