Advertisement

CNN-BD: An Approach for Disease Classification and Visualization

  • G. Madhukar RaoEmail author
  • T. Ravi Kumar
  • A. Rajashekar reddy
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)

Abstract

Data visualization is one of the complex parts of the discovery process in the current phase of big data. Finding the hidden level data of big data is that the principal goal of the classifier. The size of the data, number of classes, and the feature space had an effect on the performance of the classifiers. The new analysis of algorithms is needed for improving the accuracy, efficiency, and reliability of the classifiers. This paper proposes a Deep Learning based Convolution Neural Network classifier to classify and visualize the disease data. PCA and PSO methods are used for multivariate data analysis to handle massive data and feature selection. To demonstrate the proposed learning algorithm, real-world datasets are used. The comparative study shows that deep learning classifier performs better than other classifiers and scientifically higher.

Keywords

Big data Convolutional neural network PSO PCA Machine learning 

Notes

Acknowledgements

This work is partially supported by the Sanjivani College of Engineering, Kopargaon that is affiliated to AICTE, Government of India. The authors express their gratitude toward the Department of Computer Engineering and Information Technology at Sanjivani College of Engineering for providing all necessary support to carry out the research work.

References

  1. 1.
    G. Madhukar Rao, D. Ramesh, Supervised learning techniques for big data: a survey. IJCTA Int. Sci. Press 9, 3811–3891 (2016)Google Scholar
  2. 2.
    H. Yalcin, S. Razavi, Plant classification using convolutional neural networks. In: 2016 Fifth International Conference on AgroGeoinformatics (Agro-Geoinformatics) (2016)Google Scholar
  3. 3.
    Y. Kim, Convolutional Neural Networks for Sentence Classification, New York University, https://arxiv.org/pdf/1408.5882.pdf (2014)
  4. 4.
    W. Ari, W. Jatmiko, H. Arief Wisesa, B. Hardjono, P. Mursanto, Traffic big data prediction and visualization using fast incremental model trees-drift detection (fimt-dd). Knowl. Based Syst. 93, 33–46 (2016)Google Scholar
  5. 5.
    C.M. Farrelly, Dimensionality Reduction Ensembles. Independent researcher https://arxiv.org/ftp/arxiv/papers/1710/1710.04484.pdf
  6. 6.
    B. Xue, M. Zhang, W.N. Browne, Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. CybernGoogle Scholar
  7. 7.
    K. Vasan, B. Surendiran, Dimensionality reduction using principal component analysis network intrusion detection. Perspect. Sci. 8, 510–512 (2016)Google Scholar
  8. 8.
    Barnali Sahua, Debahuti Mishrab, A novel feature selection algorithm using particle swarm optimization for cancer microarray data (ICMOC-2012). Procedia Eng. 38, 27–31 (2012)CrossRefGoogle Scholar
  9. 9.
    B. Jan, H. Farman, M. Khan, M. Imran, I. Ul Islam, A. Ahmad, S. Ali, G. Jeon, Big data analytics using deep learning: a comparative study. Compute. Electrical Eng. (2017)Google Scholar
  10. 10.
    https://archive.ics.uci.edu/ml/datasets/ILPD (Indian Liver Patient Dataset)
  11. 11.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • G. Madhukar Rao
    • 1
    Email author
  • T. Ravi Kumar
    • 2
  • A. Rajashekar reddy
    • 3
  1. 1.Department of Computer EngineeringSanjivani College of EngineeringKopargaonIndia
  2. 2.Department of Information TechnologySanjivani College of EngineeringKopargaonIndia
  3. 3.Department of Information TechnologyBVRITHyderabadIndia

Personalised recommendations