Data Mining Techniques for Prediction of Diabetes Dieseas

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-4
Year of Publication : 2019
Authors : Miss.P. D. Bharsakle, Dr.G.R.Bamnote, Prof P.K.Agrawal
  10.14445/22312803/IJCTT-V67I4P126

MLA

MLA Style:Miss.P. D. Bharsakle, Dr.G.R.Bamnote, Prof P.K.Agrawal"Data Mining Techniques for Prediction of Diabetes Dieseas" International Journal of Computer Trends and Technology 67.4 (2019): 131-133.

APA Style: Miss.P. D. Bharsakle, Dr.G.R.Bamnote, Prof P.K.Agrawal (2019). Data Mining Techniques for Prediction of Diabetes Dieseas International Journal of Computer Trends and Technology, 67(4), 131-133.

Abstract
In recent days diabetes has become a common disease to the all peoples from young to the old persons. The growth of the diabetic patients is increasing day-by-day due to various reasons such as viral or bacterial infection, toxic or chemical contents mix with the food, auto immune reaction, fatness, irregular diet, change in lifestyles, eating habit, environment pollution, etc. therefore, diagnosing the diabetes is very essential to save the human life from diabetes. a process of examining and identifying the hidden patterns from large amount of data to draw conclusions is The data analytics. In health care, this analytical process is carried out using machine learning algorithms for analysing medical data to build the machine learning models to carry out medical diagnoses. This paper exhibits a diabetes forecast framework to conclusion diabetes. Additionally, this paper investigates the ways to deal with improve the exactness in diabetes expectation utilizing medicinal information with different AI calculations and techniques

Reference
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Keywords
Data Mining , Support Vector Machine , K-Nearest Neighbor