Predictive Methodology for Diabetic Data Analysis in Big Data

被引:63
|
作者
Kumar, Saravana N. M. [1 ]
Eswari, T. [3 ]
Sampath, P. [2 ]
Lavanya, S. [3 ]
机构
[1] Bannari Amman Insitute Technol, Dept CSE, Sathyamangalam 638401, India
[2] Bannari Amman Inst Technol, Dept CSE, Sathymangalam 628401, India
[3] Sri Krishna Coll Engn & Techechnol, Dept IT, Coimbatore 641008, Tamil Nadu, India
关键词
Healthcare industry; Hadoop/Map Reduce; Big Data; Predictive analysis;
D O I
10.1016/j.procs.2015.04.069
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Modernizing healthcare industry's move towards processing massive health records, and to access those for analysis and put into action will greatly increases the complexities. Due to the growing unstructured nature of Big Data form health industry, it is necessary to structure and emphasis its size into nominal value with possible solution. Healthcare industry faces many challenges that make us to know the importance to develop the data analytics. Diabetic Mellitus (DM) is one of the Non Communicable Diseases (NCD), is a major health hazard in developing countries such as India. The acute nature of DM is associated with long term complications and numerous of health disorders. In this paper, we use the predictive analysis algorithm in Hadoop/Map Reduce environment to predict the diabetes types prevalent, complications associated with it and the type of treatment to be provided. Based on the analysis, this system provides an efficient way to cure and care the patients with better outcomes like affordability and availability. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:203 / 208
页数:6
相关论文
共 50 条
  • [31] Protagonist of Big Data and Predictive Analytics using data analytics
    Subbalakshmi, Sakineti
    Prabhu, C. S. R.
    PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON COMPUTATIONAL TECHNIQUES, ELECTRONICS AND MECHANICAL SYSTEMS (CTEMS), 2018, : 276 - 279
  • [32] Data envelopment analysis and big data
    Khezrimotlagh, Dariush
    Zhu, Joe
    Cook, Wade D.
    Toloo, Mehdi
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2019, 274 (03) : 1047 - 1054
  • [33] Big Data Analysis Structuring Of Data
    Ojha, Riya
    Singh, Rakshit
    Singh, Aditya
    2017 INTERNATIONAL CONFERENCE ON TECHNICAL ADVANCEMENTS IN COMPUTERS AND COMMUNICATIONS (ICTACC), 2017, : 144 - 147
  • [34] Methodology for extracting the delighter in Kano model using big data analysis
    Kim, Taehun
    Yoo, Taejong
    TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE, 2020, 31 (5-6) : 654 - 665
  • [35] Study of the Predictive Mechanism with Big Data-Driven Lean Manufacturing and Six Sigma Methodology
    Chen, Hong
    Wu, Jiande
    Zhang, Wei
    Guo, Qing
    Lu, Huifeng
    ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT IV, 2021, 633 : 662 - 672
  • [36] BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture
    Morota, Gota
    Ventura, Ricardo V.
    Silva, Fabyano F.
    Koyama, Masanori
    Fernando, Samodha C.
    JOURNAL OF ANIMAL SCIENCE, 2018, 96 (04) : 1540 - 1550
  • [37] BIG DATA ANALYSIS
    Trnka, Andrej
    EUROPEAN JOURNAL OF SCIENCE AND THEOLOGY, 2014, 10 : 143 - 148
  • [38] The Big Data Analysis
    Burunova, Anna V.
    PROCEEDINGS OF THE 2018 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS), 2018, : 285 - 286
  • [39] Big data and smart computing: methodology and practice
    Rong, Chunming
    Liu, Lu
    Chen, Guolong
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2016, 28 (11): : 3077 - +
  • [40] Preface: Philosophy, Methodology, and Practice of Big Data
    Liu Y.
    Liu, Ying (yingliu@ucas.ac.cn), 1600, Springer Science and Business Media Deutschland GmbH (01): : 281 - 282