An Intelligent System for Lung Cancer Diagnosis Using a New Genetic Algorithm Based Feature Selection Method

作者：Lu, Chunhong; Zhu, Zhaomin; Gu, Xiaofeng^*

来源：Journal of Medical Systems, 2014, 38(9): 97.

DOI：10.1007/s10916-014-0097-y

• Lung cancer
• Diagnosis
• Genetic algorithm
• Feature selection
• Machine learning

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.

• 出版日期2014-9
• 单位江南大学