DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA

Vinitha S, Sweetlin S, Vinusha H and Sajini S 

Computer Science and Engineering, S.A. Engineering College, India  

ABSTRACT 

Due to big data progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of certain regional diseases, which may results in weakening the prediction of disease outbreaks. In the proposed system, it provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it use a latent factor model to rebuild the missing data. It experiment on a regional chronic illness of cerebral infarction. Using structured and unstructured data from hospital it use Machine Learning Decision Tree algorithm and Map Reduce algorithm. To the best of our knowledge in the area of medical big data analytics none of the existing work focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNNbased unimodal disease risk prediction (CNN-UDRP) algorithm. 

KEYWORDS 

Big data analytics, machine learning, healthcare.  

Original Source URL: http://aircconline.com/cseij/V8N1/8118cseij01.pdf

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Computer Science & Engineering: An International Journal (CSEIJ), Vol.8, No.1, February 2018

DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA

Vinitha S, Sweetlin S, Vinusha H and Sajini S 

Computer Science and Engineering, S.A. Engineering College, India 

ABSTRACT

Due to big data progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of certain regional diseases, which may results in weakening the prediction of disease outbreaks. In the proposed system, it provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it use a latent factor model to rebuild the missing data. It experiment on a regional chronic illness of cerebral infarction. Using structured and unstructured data from hospital it use Machine Learning Decision Tree algorithm and Map Reduce algorithm. To the best of our knowledge in the area of medical big data analytics none of the existing work focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNNbased unimodal disease risk prediction (CNN-UDRP) algorithm.

KEYWORDS

Big data analytics, machine learning, healthcare.  

1. INTRODUCTION  

With the advance of big data analytics equipment, more devotion has been paid to disease expectation from the perception of big data inquiry, various explores have been conducted by choosing the features mechanically from a large number of data to improve the truth of menace classification rather than the formerly selected physiognomies. However, those prevailing work mostly measured structured data. Thus, risk organization based on big data analysis, the following tasks remain: How should the mislaid data be lectured? How should the main chronic diseases in a positive county and the main faces of the disease in the region be gritty? How can big data analysis expertise be used to estimate the disease and generate a better method? 

To solve these problems, it see the structured and unstructured data in healthcare field to assess the risk of disease. First, the system use Decision tree map algorithm to generate the pattern and causes of disease. It clearly shows the diseases and sub diseases. Second, by using Map Reduce algorithm for partitioning the data such that a query will be analyzed only in a specific partition, which will increase the operational efficiency but reduce query retrieval time. Map reducing algorithm is used for partitioning the medical data based on the output of Decision Tree map algorithm. Compared to several typical prediction algorithms, the prediction accuracy of our proposed algorithm increases.

 

Computer Science & Engineering: An International Journal (CSEIJ)

http://airccse.org/journal/cseij/index.html

ISSN: 2231 - 329X (Online); 2231 - 3583 (Print)

                                          

Scope & Topics

Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.

The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Computer science & Computer Engineering advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science & Computer Engineering.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science & Engineering.

Topics of interest include but are not limited to, the following

·         Accessible Computing

·         Algorithms and Computation Theory

·         Artificial Intelligence and Soft Computing

·         Bioinformatics and Biosciences

·         Computer Architecture

·         Computer Graphics and Animation

·         Computer Science / Information Technology Education

·         Cryptography and Information security

·         Data Communication and Computer Networks

·         Data Mining and Knowledge Management Process

·         Database Management Systems

·         Design Automation

·         Digital Signal and Image Processing

·         Electronic Commerce

·         Embedded Systems

·         Genetic and Evolutionary Computation

·         Health Informatics

·         High Performance Computing

·         Information Retrieval

·         Internet Engineering & Web services

·         Management Information Systems

·         Measurement and Evaluation

·         Micro architecture

·         Multimedia and Applications

·         Operating Systems

·         Programming Languages

·         Security, Privacy and Trust Management

·         Simulation and Modeling

·         Software Engineering

·         Ubiquitous computing

·         Wireless and Mobile networks

Paper Submission     

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Important Dates

·         Submission Deadline :  March 17,  2018

·         Acceptance Notification : April 17,  2018

·         Final Manuscript Due      : April 25,  2018

·         Publication Date              : Determined by the Editor-in-Chief

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