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Novel Wrapper-Based Feature Selection for Efficient Clinical Decision Support System

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Advances in Data Science (ICIIT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 941))

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Abstract

Although healthcare sector has evolved with several new computer technologies it requires effective and efficient analytical techniques to truly exploit the benefits. As the industry is time sensitive in nature, there is an absolute need to perform medical diagnosis accurately without compromising the minimal time constraint. As predictive analytics is justified to be a suitable methodology that can be applied in healthcare sector, the proposed work uses the machine learning approach in a prospective way in performing effective learning of medical data. The proposed work aims to build an efficient prediction model using two novel feature selection approaches based on variants of Particle Swarm Optimization (PSO) named Particle Swarm Optimization with Digital Pheromones (PSODP) and a combination of PSO and PSODP. The research performs diagnosis for diabetic, breast cancer and chronic kidney disease data using deep learning. The proposed work shows improvement in classification accuracy with minimal time requirement compared to existing feature selection and classification techniques.

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References

  1. Alshurafa, N., Sideris, C., Pourhomayoun, M., Kalantarian, H., Sarrafzadeh, M., Eastwood, J.A.: Remote health monitoring outcome success prediction using baseline and first month intervention data. IEEE J. Biomed. Health Inform. 21(2), 507–514 (2017)

    Article  Google Scholar 

  2. Cohen, I.G., Amarasingham, R., Shah, A., Xie, B., Lo, B.: The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff. 33(7), 1139–1147 (2014)

    Article  Google Scholar 

  3. Cortada, J.W., Gordon, D., Lenihan, B.: The value of analytics in healthcare. IBM Institute for Business Value IBM, Global Business Service (2012)

    Google Scholar 

  4. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)

    Article  Google Scholar 

  5. Davis, D.A., Chawla, N.V., Blumm, N., Christakis, N., Barabasi, A.L.: Predicting individual disease risk based on medical history. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 769–778. ACM, October 2008

    Google Scholar 

  6. Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9(1), 1 (2017)

    Google Scholar 

  7. Gunasundari, S., Janakiraman, S., Meenambal, S.: Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst. Appl. 56, 28–47 (2016)

    Article  Google Scholar 

  8. Ha, J.W., et al.: Predicting high-risk prognosis from diagnostic histories of adult disease patients via deep recurrent neural networks. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 394–399. IEEE, February 2017

    Google Scholar 

  9. https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/

  10. Kalivarapu, V.K.: Improving solution characteristics of particle swarm optimization through the use of digital pheromones, parallelization, and graphical processing units (GPUs) (2008)

    Google Scholar 

  11. Kalivarapu, V., Winer, E.: A statistical analysis of particle swarm optimization with and without digital pheromones. In: 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, p. 1882, April 2007

    Google Scholar 

  12. Kalivarapu, V., Foo, J.L., Winer, E.: Improving solution characteristics of particle swarm optimization using digital pheromones. Struct. Multidiscip. Optim. 37(4), 415–427 (2009)

    Article  Google Scholar 

  13. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2011)

    Google Scholar 

  14. Kumar, V., Minz, S.: Feature selection. SmartCR 4(3), 211–229 (2014)

    Article  Google Scholar 

  15. Moradi, P., Gholampour, M.: A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl. Soft Comput. 43, 117–130 (2016)

    Article  Google Scholar 

  16. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)

    Article  Google Scholar 

  17. Rao, V., Naresh Kumar, M.: Novel approaches for predicting risk factors of atherosclerosis (2015). arXiv preprint: arXiv:1501.07093

  18. Sahoo, P.K., Mohapatra, S.K., Wu, S.L.: Analyzing healthcare big data with prediction for future health condition. IEEE Access 4, 9786–9799 (2016)

    Article  Google Scholar 

  19. Tran, B., Zhang, M., Xue, B.: A PSO based hybrid feature selection algorithm for high-dimensional classification. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3801–3808. IEEE, July 2016

    Google Scholar 

  20. Walter, B., Sannier, A., Reiners, D., Oliver, J.: UAV swarm control: calculating digital pheromone fields with the GPU. J. Def. Model. Simul. 3(3), 167–176 (2006)

    Article  Google Scholar 

  21. Xi, M., Sun, J., Liu, L., Fan, F., Wu, X.: Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine. Comput. Math. Methods Med. 2016, 1–9 (2016)

    Article  Google Scholar 

  22. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)

    Article  Google Scholar 

  23. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl. Soft Comput. 18, 261–276 (2014)

    Article  Google Scholar 

  24. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  25. Zhang, Y., Gong, D.W., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 14(1), 64–75 (2017)

    Article  Google Scholar 

  26. https://archive.ics.uci.edu/ml/datasets.html

  27. Vanaja, R., Saswati, M.: An effective clinical decision support system using swarm intelligence. Paper accepted and Abstract published in the proceedings of 1st International Symposium on Artificial Intelligence and Computer Vision, Chennai, Tamilnadu, India (2018). ISBN 978-93-84389-18-5

    Google Scholar 

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Acknowledgement

We would like to thank and acknowledge the “Visvesvaraya PhD Scheme”, MeitY, New Delhi for supporting the research financially in the form of scholarship.

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Authors and Affiliations

  1. Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India

    R. Vanaja & Saswati Mukherjee

Authors
  1. R. Vanaja
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  2. Saswati Mukherjee
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Corresponding author

Correspondence to R. Vanaja .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Leman Akoglu

  2. University of Southern California, Marina Del Rey, CA, USA

    Emilio Ferrara

  3. CEG, Anna University, Chennai, India

    Mallayya Deivamani

  4. Northeastern University at Silicon Valley, San Jose, CA, USA

    Ricardo Baeza-Yates

  5. Anna University, Chennai, India

    Palanisamy Yogesh

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Vanaja, R., Mukherjee, S. (2019). Novel Wrapper-Based Feature Selection for Efficient Clinical Decision Support System. In: Akoglu, L., Ferrara, E., Deivamani, M., Baeza-Yates, R., Yogesh, P. (eds) Advances in Data Science. ICIIT 2018. Communications in Computer and Information Science, vol 941. Springer, Singapore. https://doi.org/10.1007/978-981-13-3582-2_9

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  • DOI: https://doi.org/10.1007/978-981-13-3582-2_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3581-5

  • Online ISBN: 978-981-13-3582-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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