IJMTES – MULTI DISEASE ANALYSIS USING DATA MINING TECHNIQUE WITH CLUSTERING AND CLASSIFICATION

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : MULTI DISEASE ANALYSIS USING DATA MINING TECHNIQUE WITH CLUSTERING AND CLASSIFICATION

Author’s Name : A Sindhujaa | V Sujatha
unnamed

Volume 04 Issue 06 2017

ISSN no:  2348-3121

Page no: 18-22

Abstract – Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. The greatest challenges is to deal with large dataset with high amount of dimensionality, together in terms of the number of features the data has, as well the number of rows of data that user is dealing with C4.5 Algorithm is a machine learning tool that is capable of performing these tasks. This paper presents an integrative approach to predict the diabetic disease from clinical big data. The clinical database is generally redundant, incomplete, vague and unpredictable. The main objective of this project is clustering the big data using fuzzy c means and feature extraction is done by principle component analysis (PCA) and classify based on C4.5 algorithm.

References

  1. Michael Minelli, Michele Chambers, Ambiga Dhiraj. Big Data Big analytics: emerging business intelligence and analytics trend for today’s businesses, feb 2013.
  2. Parkinson’s disease, challenges, progress and promise: National Institute Of Neurological Disorder And Stroke, National Institute of Health, November 2004.
  3. Jiawai Han and Micheline Kamber. Data Mining Concepts and Techniques: second edition.
  4. Pravin Kumar and Vijay Singh Rathore. Efficient Capabilities of Processing of Big Data using Hadoop MapReduce: International Journal of Advanced Research in Computer and Communication Engineering June 2014; Vol: 3, Issue 6.
  5. Wei Dai and Wei Ji. A MapReduce Implementation of C4.5 Decision Tree Algorithm: International Journal of Database Theory and Application; Vol: 7, No.1 (2014), pp.49-60.
  6. TawseefAyoub Shaikh. A Prototype of Parkinson’s and Primary Tumor Diseases Prediction Using Data Mining Techniques: International Journal of Engineering Science Invention April 2014, Vol: 3 Issue 4, pp. 23-28.
  7. Anil Radhakrishnan and kirankalmadi.Big Data Medical Engine in the cloud (BDMEiC): your new Health Doctor: vol: 11 Nov 1, 2013.
  8. DivyaTomar and Sonali Agarwal. A survey on Data Mining approaches for Healthcare: International Journal of Bio-Science and Bio-Technology 2013, vol: 5.
  9. GeethaRamani R, Sivagami G, ShomanaGracia Jacob. Feature Relevance Analysis and Classification of Parkinson’s disease Tele-Monitoring data Through Data Mining: International Journal of Advanced