Machine Learning for Cancer Subtype Prediction with FSA Method

Yan Liu, Xu Dong Wang, Meikang Qiu, Hui Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Recent research demonstrates that gene expression based cancer subtype classification has more advantages over the traditional classification. However, since this kind of data always has thousands of features, performing classification is impossible by human beings without efficient and accurate algorithms. This paper reports an empirical study that explores the problem of finding a highly-efficient and accurate machine learning method on human cancer subtype classification based on the gene expression data in cancer cells. Several machine learning algorithms are well developed to solve this kind of problems, including Naive Bayes Classifier, Support Vector Machine (SVM), Random Forest, Neural Networks. Here we generate two prediction models using SVM and Random Forest algorithms along with a feature selection approach (FSA) to predict the subtype of lung cell lines. The accuracy of the two prediction models is close with a rate of more than 90%. However, the running time of SVM is much shorter than that of Random Forest.

Original languageEnglish (US)
Title of host publicationSmart Computing and Communication - 4th International Conference, SmartCom 2019, Proceedings
EditorsMeikang Qiu
Number of pages11
ISBN (Print)9783030341381
StatePublished - 2019
Event4th International Conference on Smart Computing and Communications, SmartCom 2019 - Birmingham, United Kingdom
Duration: Oct 11 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Smart Computing and Communications, SmartCom 2019
Country/TerritoryUnited Kingdom


  • Cancer subtype
  • Feature selection
  • Machine learning
  • Random Forest
  • Support Vector Machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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