Hybrid Technique for Medical Data Classification using Multi-Layer Perceptron with NB Classifier
Thalakola Syam Sundara Rao1, Bhanu Prakash Battula2
Thalakola Syam Sundara Rao*, Research Scholar, Department of CSE, Acharya Nagarjuna University, Guntur, Andhra Pradesh.
Dr Bhanu Prakash Battula, Professor & Head, Department of CSE, Tirumala Engineering College, Guntur,Andhra Pradesh, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2627-2632 | Volume-8 Issue-12, October 2019. | Retrieval Number: K21790981119/2019©BEIESP | DOI: 10.35940/ijitee.A3912.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medical data analysis gains more interest from the last decade due to its significance advantages. Medical data is a heterogeneous data, which is the combination of text data, numeric data and image data. For to analyze such heterogeneous data traditional data analysis mechanisms are inefficient. To handle this heterogeneous data deep learning is obvious choice. Deep learning is able to handle text, numeric and image data more efficiently than traditional data mining techniques. In this paper we proposed a deep learning based multilayer perceptron to analysis medical data. This method independently address the text data, image data and numerical data and combinable made medical data classification.
Keywords: Medical Data, Multi-layer Perception, Deep Learning, Medical text, Medical Images.
Scope of the Article: Classification