A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection

被引：149

作者：

Maleki N. ^{[1
]}

Zeinali Y. ^{[2
]}

Niaki S.T.A. ^{[2
]}

机构：

[1] Department of Industrial Engineering, Faculty of Engineering, University of Tehran

[2] Department of Industrial Engineering, Sharif University of Technology, Tehran

来源：

Expert Systems with Applications | 2021年 / 164卷

关键词：

Cancer staging diagnosis; Data mining; Feature selection; Genetic algorithm; k-NN technique; Lung cancer;

D O I：

10.1016/j.eswa.2020.113981

中图分类号：

学科分类号：

摘要：

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one could use the algorithm to find a correlation between the clinical information and data mining techniques to support lung cancer staging diagnosis efficiently. © 2020 Elsevier Ltd

引用

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