Early Prediction of Diabetic Using Data Mining

Author:

Jaber Fayzeh Abdulkareem,James Joy WinstonORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference16 articles.

1. Bharath C, Saravanan N, Venkatalakshmi S. Assessment of knowledge related to diabetes mellitus among patients attending a dental college in Salem city-A cross sectional study. Braz Dent Sci. 2017;20:93–100.

2. Dominic V, Gupta D. Khare S. Aggarwal A. Investigation of chronic disease correlation using data mining techniques. In: Proceedings of the 2015 2nd international conference on recent advances in engineering & computational sciences (RAECS), Chandigarh, India, 21–22 December 2015, pp 1–6.

3. Priyadarshini K, Lakshmi I. Predictive analysis of diabetes using Bayesian network and naive Bayes techniques. In: International conference on advancements in computing technologies - ICACT 2018, vol 4, issue 2. ISSN: 2454-4248.

4. Jegan C. Classification of diabetes disease using support vector machine. Int J Eng Res Appl. 2013;3:1797–801.

5. Soni U, Behara S, Unni Krishnan K, Kumar R. Application of association rule mining in risk analysis for diabetes mellitus. Int J Adv Res Comput Commun Eng. 2016;5(4). ISSN (Online) 2278-1021. ISSN (Print) 2319 5940.

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