DATA MINING TECHNIQUES FOR THE ANALYSIS OF TYPE-2 DIABETES–A SURVEY

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Invigouranxiety businesses, data mining plays an important role in early prophecy of diseases. Numerous tests must be conducted in a patient to detect a disease. Data mining techniques is used in disease prediction to reduce the test and increase the accuracy of rate of detection. Diabetes mellitus is one of the most common diseases among adolescent. This develops at a middle age and more common in overweight children and youngsters. so as to reduce the population with diabetes mellitus it should be detected at an earlier stage.This paper explore the survey on forecasting of diabetes using different data mining techniques such as Fuzzy Logic,Naive Bayes,J48 (C4.5), JRip, Neural networks, Decision trees.

References

[1]PardhaRepalli, “Prediction on Diabetes Using Data mining Approach”.

[2]G. Parthiban, A. Rajesh, S.K.Srivatsa, “Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method “, International Journal ofComputer Applications (0975 – 8887) Volume 24– No.3, June 2011.

[3]Lin, C., Lee, C., “Neural Fuzzy Systems,” PrenticeHall, NJ, 1996.

[4]Tsoukalas, L., Uhrig, R., “Fuzzy and Neural Approaches in Engineering,” John Wiley & Sons, Inc., NY, 1997.

[5] https://en.wikipedia.org/wiki/Diabetes_mellitus.

[6] Cortes, C., Vapnik, V., "Support Vector Networks," Machine Learning,20:273?297, (1995).

[7] P. Radha, Dr. B. Srinivasan, “ Predicting Diabetes by consequencing the various Data mining Classification Techniques”, International Journal of Innovative Science, Engineering &Technology, vol. 1 Issue 6, August 2014, pp. 334-339.

[8]Murat Koklu and YauzUnal, “ Analysis of a population of Diabeticpatients Databases with Classifiers”,International Journal of Medical,Health,Pharmaceutical and Biomedical Engineering”, vol.7 No.8, 2013.

[9]Riccardo Bellazzi and Ameen Abu-Hanna. 2009. Data mining technologies for blood glucose and diabetes management. Journal of diabetes science and technology. 3(3): 603-612. PMID: 20144300.

[10]ChaitraliDangare, S. and SulabaApte,S.”Improved study of disease prediction using data mining classification techmiques”.Int.J.Comp.Appl.,2012,47(10):75-88.

[11]Yang Guo,GuohuaBai,Yan Hu School of computing Blekinge Institute of Technology Karlskrona,Sweden, “Using Bayes Network for prediction of Type-2 Diabetes”.

Keywords

Diabetes Mellitus,FuzzyLogic,Naive Bayes, J48 (C4.5), JRip, Neural networks, Decision tree.