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.
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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.
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Madhukar Rao, G., Ravi Kumar, T., Rajashekar reddy, A. (2020). CNN-BD: An Approach for Disease Classification and Visualization. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_14
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DOI: https://doi.org/10.1007/978-981-15-0978-0_14
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