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Genes & Genomics

, Volume 41, Issue 11, pp 1301–1313 | Cite as

Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

  • Lokeswari VenkataramanaEmail author
  • Shomona Gracia Jacob
  • Rajavel Ramadoss
  • Dodda Saisuma
  • Dommaraju Haritha
  • Kunthipuram Manoja
Research Article
  • 71 Downloads

Abstract

Background

Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming. Hence parallel programming frameworks like Hadoop, Spark and Mahout are necessary to ease the task of computation.

Objective

Not all the gene expressions are necessary in prediction, it is very essential to select important genes for improving classification accuracy. So feature selection algorithms are parallelized and executed on Spark framework to eliminate unnecessary genes and identify only predictive genes in very less time without affecting prediction accuracy.

Methods

Parallelized hybrid feature selection (HFS) method is proposed to serve the purpose. This method includes parallelized correlation feature subset selection followed by rank-based feature selection methods. The selected subset of genes is evaluated using parallel classification algorithms. The accuracy values obtained are compared with existing rank-weight feature selection, parallelized recursive feature selection methods and also with the values obtained by executing parallelized HFS on DistributedWekaSpark.

Results

The classification accuracy obtained with the proposed parallelized HFS method is 97% and 79% for gastric cancer and childhood leukemia respectively. The proposed parallelized HFS method produced ~ 4% to ~ 15% improvement in classification accuracy when compared with previous methods.

Conclusion

The results reveal the fact that the proposed parallelized feature selection algorithm is scalable to growing medical data and predicts cancer sub-types in lesser time with higher accuracy.

Keywords

Parallelized hybrid feature selection Correlation feature subset selection Rank-based methods Parallel classification Spark DistributedWekaSpark 

Notes

Acknowledgements

This research work is part of project work funded by Science and Engineering Research Board (SERB), Department of Science and Technology (DST) funded project under Young Scientist Scheme—Early Start-up Research Grant- titled “Investigation on the effect of Gene and Protein Mutants in the onset of Neuro-Degenerative Brain Disorders (Alzheimer’s and Parkinson’s disease): A Computational Study” with Reference no-SERB—YSS/2015/000737/ES.

Compliance with ethical standards

Conflict of interest

Lokeswari Venkataramana, Shomona Gracia Jacob, Rajavel Ramadoss, Dodda Saisuma, Dommaraju Haritha and Kunthipuram Manoja declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent is not necessary as this article does not involve human or animal participants.

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Copyright information

© The Genetics Society of Korea 2019

Authors and Affiliations

  1. 1.Department of CSESri Sivasubramaniya Nadar College of EngineeringChennaiIndia
  2. 2.MuscatOman
  3. 3.Department of ECESri Sivasubramaniya Nadar College of EngineeringChennaiIndia

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