A Survey on Different Data Mining Techniques for Early Prediction of Diabetes

Authors

  • Pallavi Shaniware  Department of Information Technology, St. Vincent Pallotti college of Engineering and Technology, Nagpur, Maharashtra, India
  • Manasi Paripelli  Department of Information Technology, St. Vincent Pallotti college of Engineering and Technology, Nagpur, Maharashtra, India
  • Anubhav Pareek  Department of Information Technology, St. Vincent Pallotti college of Engineering and Technology, Nagpur, Maharashtra, India
  • Saurabh Gupta  Department of Information Technology, St. Vincent Pallotti college of Engineering and Technology, Nagpur, Maharashtra, India
  • Prof. Praveen Sen  Assistant Professor, Department of Information Technology, St. Vincent Pallotti college of Engineering and Technology, Nagpur, Maharashtra, India

Keywords:

Data mining, Diabetes Prediction, Body Mass Index, Association Rule Mining, Bottom up Summarization

Abstract

Data mining assumes a proficient part in prediction of maladies in medicinal services industry. Diabetes is one of the major worldwide medical issues. As indicated by WHO 2014 report, around 422 million individuals worldwide are experiencing diabetes. Diabetes is a metabolic sickness where the uncalled for administration of blood glucose levels prompted danger of numerous infections like heart assault, kidney ailment, eye and so forth. Numerous calculations are produced for prediction of diabetes and exactness estimation yet there is no such calculation which will give seriousness regarding proportion translated as effect of diabetes on various organs of human body. This paper gives definite audit of existing data mining strategies utilized for prediction of diabetes. It likewise gives future heading for seriousness estimation of diabetes.

References

  1. GyorgyJ.Simon,Pedro J.Caraballo,Terry M. Therneau,Steven S. Cha, M. Regina Castro and Peter W.Li “Extending Association Rule Summarization Techniques to Assess Risk Of Diabetes Mellitus,” IEEE Transanctions on Knowledge and Data Engineering ,vol 27,No.1,January 2015
  2. Dr.Zuber khan, shaifali singh and Krati Sexena,” Diagnosis of Diabetes Mellitus using K- Nearest Neighbor Algorithm,” in proceeding of International Journal of Computer Science Trends and Technology, vol.2 , July-Aug 2014
  3. Mukesh kumari and Dr. Rajan Vohra,“Prediction of Diabetes Using Bayesian Network,”in proceeding of International Journal of Computer Science and Information Technologies, vol. 5 , 2014
  4. Asha Gowda Karegowda ,A.S. Manjunath , M.A. Jayaram,‖Application Of Genetic Algorithm Optimized Neural Network Connection Weights For Medical Diagnosis Of Pima Indians Diabetes,‖ International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011.
  5. Ravi Sanakal, Smt. T Jayakumari, Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine,‖ International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 2 May 2014
  6. Dr. Pramanand Perumal and Sankaranarayanan, “Diabetic prognosis through Data Mining Methods and Techniques,” in proceeding of International Conference on Intelligent Computing Applications, vol. 2, 2014
  7. Satyanarayana Gandi and Amarendra Kothalanka,“An Efficient Expert System For Diabetes By Naïve Bayesian Classifier,” in proceeding of International Journal of Engineering Trends and Technology ,vol. 4 ,Issue 10 , Oct 2013
  8. Paul S. Heckerling, Gay J. Canaris, Stephen , Flach, Thomas G. Tape,Robert S. Wigton, Ben S. Gerber, Predictors of urinary tract infection based on artificial neural networks and genetic algorithms, international journal of medical informatics 7 6, 2007
  9. Dilip Kumar Choubey and Sanchita Paul,”GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis”,in proceedings of I.J.Intelligent System and Applications, vol.1,pp.49-59,2016
  10. Alan J. Garber,MD and Martin J.Abrahamson, Case study on ”AACE/ACE Comprehensive Diabetes Management Algorithm”
  11. Ramkrishnan Shrikant and Rakesh Agrawal,“Fast Algorithms for mining association rule,” in proceeding of IEEE International Conference on Data Engineering,vol.16,2007
  12. Kawita Rawat and Kawita Bhurse” A Comparative Approach for Pima Indians Diabetes Diagnosis using LDA-Support Vector Machine and Feed Forward Neural Network,”in proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, Nov. 2014
  13. G. S Collins, S. Mallett, O. Omar, and L.-M. Yu, “Developing risk prediction models for type 2 diabetes: A systematic review of methodology and reporting,”in proceedings of BMC Med., 9:103,Sept. 2011
  14. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proceedings of 20th VLDB, Santiago, Chile, 1994
  15. M. A. Hasan, “Summarization in pattern mining,” in proceedings of Encyclopedia of Data Warehousing and Mining, 2nd ed. Hershey, PA, USA:Information Science Reference, 2008
  16. R.P.Bakshi and S.Agrawal,”Modeling Risk of Prediction of Diabetes - a preventive Measure,” in proceedings of BMC Med., 2012.
  17. S.Sapna and Dr.A.Tamilarasi,”Implementation of Genetic algorithm in Predicting Diabetes” in Proceedings of International journal of Computer science ,vol.9,Issue.1,N0.3,Jan-2012.
  18. S. Nagarajan and R.M.Chandrasekaran,”Data Mining Techniques for Performance Evaluation of Diagnosis in Gestational Diabetes” in proceedings of International Journal of Current Research and academic Review, vol. 2,No. 10,pp. 91-98.
  19. V.Vijayan and A.Ravikumar,” Study of data mining algorithms for Prediction and diagnosis of diabetes mellitus,” in proceedings of International Journal of Computer Application, vol. 9,No. 17, June 2014.
  20. J.Tuomilehto, “Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impared glucose tolerance”,in proceedings of International Journal of Medical Research, vol. 344,no. 18,pp. 1343-1350, 2001.
  21. K.Rajesh and V.Sangeetha,”Application of Data Mining Methods and Techniques for Diabetes Diagnosis,” in proceedings of International journal of Engineering and Innovative Technology, vol.2, Issue 3, September 2012.
  22. B.L. Shivkumar and S Alby,”A Survey on Data Mining Technologies for Prediction and Diagnosis of Diabetes,” in proceedings of International Conference on Intelligent Computing Application, 2014.
  23. Carlos Fernandez_Llatas and Antonio Martinez_Millanu, “Diabetes care related process modelling using Process Mining Techniques Lessons Learned in the Application of Interactive Pattern Recognition: Coping with the Spaghetti Effect, 2015.

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Published

2017-12-31

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Section

Research Articles

How to Cite

[1]
Pallavi Shaniware, Manasi Paripelli, Anubhav Pareek, Saurabh Gupta, Prof. Praveen Sen, " A Survey on Different Data Mining Techniques for Early Prediction of Diabetes , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.1283-1289, November-December-2017.