Data Mining Techniques for Prediction of Diabetes Dieseas
 |
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 | ||
| DOI : 10.14445/22312803/IJCTT-V67I4P126 |
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
[1] B.Tamilvanan1, Dr.V. Murali Bhaskaran2,”An Experimental Study of Diabetes Disease Prediction System Using Classification Techniques”
[2] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly,” DIAGNOSIS OF DIABETES USING
[3] CLASSIFICATION MINING TECHNIQUES”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.1, January 2015
[4] Basharat Naqvi, Arshad Ali, Muhammad Adnan Hashmi and Muhammad Atif”Prediction Techniques for Diagnosis of Diabetic Disease: A Comparative Study,”
[5] Kaveeshwar, S.A., and Cornwall, J., 2014, “The current state of diabetes mellitus in India”. AMJ, 7(1), pp. 45-48.
[6] Dean, L., McEntyre, J., 2004, “The Genetic Landscape of Diabetes [Internet]. Bethesda (MD): National Center for Biotechnology Information (US);. Chapter 1, Introduction to Diabetes. 2004 Jul 7.
[7] Mohammed, A.K., Sateesh, K. P., Dash G. N., 2013, “A Survey of Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases” International Journal of Advanced Research in Computer Science and Software Engineering, 3(8), pp. 149-153.
[8] Chunhui, Z., Chengxia, Y., 2015, “Rapid Model Identification for Online Subcutaneous Glucose Concentration Prediction for New Subjects with Type I Diabetes”, IEEE Transactions on Biomedical Engineering, 62 (5), pp. 1333 – 1344
[9] Vaishali, A., Harsh, K., Anil, K.A, 2016, “Performance Analysis of the Competitive Learning Algorithms on Gaussian Data in Automatic Cluster Selection”, 2016 Second International Conference on Computational Intelligence & Communication Technology.
Keywords
Data Mining , Support Vector Machine , K-Nearest Neighbor