A Survey on Prediction of Diabetes using Data Mining

Authors(5) :-Shravani S. Shinde, Rohini M. Rajmane, Shradhda S. Chindage, Shweta S. Gundale, Uday B. Mane

With recent advances in computer technology, large amounts of data will be collect and store, but all this data becomes more useful when it is analyze and some dependencies and correlations has found. This should be an accomplished with machine learning algorithms. The automatic diagnosis of diabetes is an important real-world medical problem. Detection of diabetes in its early stages is the key for treatment. A study of existing systems reveals that it is important to discover the hidden knowledge from a particular dataset to improve the quality of health care for diabetic patients. Techniques used in existing systems are namely: EM algorithm, H-means+ clustering and Genetic Algorithm, for the classification of the diabetic patients. In addition, Ant Colony Optimization (ACO) has used in data mining field to extract rule based on classification systems. Survey work shows that there are data mining algorithms which can be used in the analysis to develop a more efficient prediction technique.

Authors and Affiliations

Shravani S. Shinde
Computer Science & Engineering, Shivaji University/Sanjay Ghodawat Institute, Atigre/ Kolhapur, Maharashtra, India
Rohini M. Rajmane
Computer Science & Engineering, Shivaji University/Sanjay Ghodawat Institute, Atigre/ Kolhapur, Maharashtra, India
Shradhda S. Chindage
Computer Science & Engineering, Shivaji University/Sanjay Ghodawat Institute, Atigre/ Kolhapur, Maharashtra, India
Shweta S. Gundale
Computer Science & Engineering, Shivaji University/Sanjay Ghodawat Institute, Atigre/ Kolhapur, Maharashtra, India
Uday B. Mane
Computer Science & Engineering, Shivaji University/Sanjay Ghodawat Institute, Atigre/ Kolhapur, Maharashtra, India

With recent advances in computer technology, large amounts of data will be collect and store, but all this data becomes more useful when it is analyze and some dependencies and correlations has found. This should be an accomplished with machine learning algorithms. The automatic diagnosis of diabetes is an important real-world medical problem. Detection of diabetes in its early stages is the key for treatment. A study of existing systems reveals that it is important to discover the hidden knowledge from a particular dataset to improve the quality of health care for diabetic patients. Techniques used in existing systems are namely: EM algorithm, H-means+ clustering and Genetic Algorithm, for the classification of the diabetic patients. In addition, Ant Colony Optimization (ACO) has used in data mining field to extract rule based on classification systems. Survey work shows that there are data mining algorithms which can be used in the analysis to develop a more efficient prediction technique.

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 561-564
Manuscript Number : CSEIT1833108
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Shravani S. Shinde, Rohini M. Rajmane, Shradhda S. Chindage, Shweta S. Gundale, Uday B. Mane , "A Survey on Prediction of Diabetes using Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.561-564, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833108

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