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

被引：135
|
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
收藏
相关论文
共 50 条
  • [1] Detection of Cancer in Lung With K-NN Classification Using Genetic Algorithm
    Bhuvaneswari, P.
    Therese, A. Brintha
    2ND INTERNATIONAL CONFERENCE ON NANOMATERIALS AND TECHNOLOGIES (CNT 2014), 2015, 10 : 433 - 440
  • [2] Feature Selection by Using DE Algorithm and k-NN Classifier
    Senel, Fatih Ahmet
    Yuksel, Asim Sinan
    Yigit, Tuncay
    ARTIFICIAL INTELLIGENCE AND APPLIED MATHEMATICS IN ENGINEERING PROBLEMS, 2020, 43 : 886 - 893
  • [3] USING K-NN WITH WEIGHTS TO DETECT DIABETES MELLITUS BASED ON GENETIC ALGORITHM FEATURE SELECTION
    Shu, Ting
    Zhang, Bob
    Tang, Y. Y.
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR), 2016, : 12 - 17
  • [4] Combining feature selection with feature weighting for k-NN classifier
    Bao, YG
    Du, XY
    Ishii, N
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2002, 2002, 2412 : 461 - 468
  • [5] A New Method For Selection Optimum k Value In k-NN Classification Algorithm
    Maleki, Masoud
    Eroglu, Kubra
    Aydemir, Onder
    Manshoori, Negin
    Kayikcioglu, Temel
    2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
  • [6] A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method
    Xiang, Jie
    Han, XiaoHong
    Duan, Fu
    Qiang, Yan
    Xiong, XiaoYan
    Lan, Yuan
    Chai, Haishui
    APPLIED SOFT COMPUTING, 2015, 31 : 293 - 307
  • [7] An automatic selection method of k in k-NN classifier
    Du, L. (dulei.323@stu.xjtu.edu.cn), 2013, Northeast University (28):
  • [8] Exploring the Feature Selection of the EEG Signal Time and Frequency Domain Features for k-NN and Weighted k-NN
    Diah, Theresia K.
    Faqih, Akhmad
    Kusumoputro, Benyamin
    PROCEEDINGS OF 2019 IEEE R10 HUMANITARIAN TECHNOLOGY CONFERENCE (IEEE R10 HTC 2019), 2019, : 196 - 199
  • [9] Using Genetic Algorithm to Improve Fuzzy k-NN
    Zhang Juan
    Niu Yi
    He Wenbin
    2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, VOLS 1 AND 2, PROCEEDINGS, 2008, : 475 - +
  • [10] Efficient Selection Algorithm for Fast k-NN Search on GPUs
    Tang, Xiaoxin
    Huang, Zhiyi
    Eyers, David
    Mills, Steven
    Guo, Minyi
    2015 IEEE 29TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), 2015, : 397 - 406
12345