EXPLORING ON CLASSIFICATION AND PREDICTION OF DIABETICS USING DATA MINING AND NEURAL NETWORK
Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Data mining is the process of pattern discovery and extraction where huge amount of data is involved. Its applications can greatly benefit all parties involved in the healthcare industry. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. It provides the methodology and technology to transform these mounds of data into useful information for decision making. Different techniques are used to find interesting patterns for medical diagnosis and treatment. Diabetes is a group of meta bolic disease in which there are high blood sugar levels over a prolonged period. Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. This paper concentrates on the overall literature survey related to various data mining techniques for predicting diabetes. This would help the researchers to know various data mining algorithm and method for the prediction of diabetes mellitus. In regard to this emerge, we have reviewed the various paper involved in this field in terms of method, algorithms and results.
1.Neha Shukla and Dr. Meena Arora, “Random Forest v/s Scaled Conjugate Gradient To Predict Diabetes Mellitus”, International Journal of Computational IntelligenceResearchISS0973-1873, ResearchIndiaPublications: http://www.ripublication.com
2.Research on BioMedical engineering rbejournal.org Volume 31, Number 2, p. 97-106, 2015, “Diabetes classification using a redundancy reduction preprocessor”.
3.M. - Mounika et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol, “Predictive Analysis of Diabetic Treatment Using Classification Algorithm”.
4.Dr. V.Srividhya G.Keerthana et al. / International Journal of Computer Science Engineering (IJCSE) “Performance Enhancement of Classifiers using Integration of Clustering and Classification Techniques”
5.International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012) “ An Improved Data Mining Model to Predict the Occurrence of Type-2 Diabetes using Neural Network”.
6.Centers for disease control and prevention, national diabetes, fact sheet, www.cdc.gov/diabetes (2011).
7.IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Online): 1694-0814 www.IJCSI.org. “Implementation of Genetic Algorithm in Predicting Diabetes” S.Sapna1, Dr.A.Tamilarasi2 and M.Pravin Kumar3.
8.Han, J.,&Micheline, K. (2006). Data mining: Concepts and Techniques, Morgan Kaufmann .Publisher.
9.Jun12doi:10.4028/www.scientific.net/JERA.24.137 Accepted: 2016-04-13. FFBAT- Optimized Rule Based Fuzzy Logic Classifier for Diabetes.
10.Egyptian Informatics Journal (2014) 15, 129–147 2014 “ A novel Neuro-fuzzy classification technique for data mining” Soumadip Ghosh .
Data Mining, Classification Techniques, Diabetes Mellitus, Neural Network and Health Care.