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Swarm Intelligence Based Feature Selection Algorithms and Classifiers for Gastric Cancer Prediction

  • L. TharaEmail author
  • R. GunasundariEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Recently, it is observed in the research domain of computer science that, data mining has emerged to be an interesting area of research constantly. It is exploited to a considerable degree in the healthcare industry, in creating patient – oriented healthcare systems and helping the health experts. This strategy has also helped in cutting down the cost factor. Gastric Cancer acquires the fourth position of generic cancer and has become the second biggest reason for mortality due to cancer in the entire world, which forms the motivating force behind this research. This technical work is aimed at the design and development of novel classifiers depending on data mining techniques for gastric cancer data classification. In addition, novel feature selection techniques are developed for the prediction of gastric cancer. The performance metrics including accuracy, hit rate and elapsed run time are computed for assessment purposes.

Keywords

Data mining Gastric cancer Health care Feature selection Classifier Dataset Performace analysis Accuracy Hit rate Elapsed time 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePSG College of Arts & Science, Research scholar of KAHECoimbatoreIndia
  2. 2.Department of Information TechnologyKarpagam Academy of Higher EducationCoimbatoreIndia

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