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Diagnosis of coronary heart disease based on 1 H NMR spectra of human blood plasma using genetic algorithm‐based feature selection
Author(s) -
Vasighi Mahdi,
Zahraei Ali,
Bagheri Saeed,
Vafaeimanesh Jamshid
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2517
Subject(s) - feature selection , classifier (uml) , artificial intelligence , pattern recognition (psychology) , linear discriminant analysis , computer science , coronary heart disease , mathematics , medicine , cardiology
1 H NMR spectroscopy was used for the diagnosis of coronary heart disease (CHD) by using human blood plasma samples. One‐dimensional 1 H 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 for discrimination between pathological and normal samples. Because of high similarity between 1 H 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. Copyright © 2013 John Wiley & Sons, Ltd.