High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Disease Prediction by CNN-EMDRP over Bigdata using MBSGD

Author(s):

U. M. Prakash , SRM INSTITUTE OF SCIENCE AND TECHNOLOGY; P.Renuka Devi, PAAVAI ENGINEERING COLLEGE NAMAKKAL; M.Sasirekha, PAAVAI ENGINEERING COLLEGE NAMAKKAL

Keywords:

CNN-MDRP, Machine Learning, Naive Bayesian, Structured and Unstructured Data

Abstract

An overview of recent developments in big data in the context of biomedical and health informatics is discussed in this project. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured and comparative study of two technique of CNN(CNN-UDRP and CNN-MDRP). Each of these techniques are efficient in their working but on comparison there is lot of difference in their working in the field of early prediction of disease. So a new convolutional neural network (CNN)-based efficient multimodal disease risk prediction algorithm (CNN-EMDRP) is proposed. This project works on the machine learning algorithms, a convolutional machine learning algorithm Naïve Bayesian (NB) is used in this project. The main purpose of this work is to propose the best technique for early prediction of disease and to reduce the diagnosis time and improve the efficiency and accuracy.

Other Details

Paper ID: IJSRDV6I20611
Published in: Volume : 6, Issue : 2
Publication Date: 01/05/2018
Page(s): 997-1001

Article Preview

Download Article