Type 2 Diabetic Prediction Using Machine Learning Algorithm

Authors

  • Sreeja Vishaly M Cognizant Technology Solutions, Coimbatore, India
  • Umamaheswari k Department of Information Technology,PSG College of Technology Coimbatore, India

Keywords:

Machine Learning, Supervised Model, Weighted KNN, Type 2 Diabetes.

Abstract

Diabetes mellitus is one of the most important chronic disease  and  has  become  a  major  public  health challenge in the recent world. Currently Machine Learning approaches have been used to analyze and  predict the probability of people getting affected by diabetes. Diabetes can  be effectively identified using the proposed Machine  Learning  technique.  Many techniques  and algorithms were used before for the  prediction of  type 2 diabetes  prediction ,one such model was Multinomial Logistic Regression .Moving a step ahead to improve the diagnostic efficiency, this paper  proposes the use of  Weighted K –Nearest  neighbor  for detecting  the  type-2 Diabetes.  This new approach proves higher effectiveness when compared to Multinomial Logistic Regression. Using Pima Indian Dataset the experiments were performed and it shows that efficiency is higher for weighted KNN when compared to Multinomial Logistic Regression. 

References

[1] Janani Priya R, Umamaheswari K, Type 2 Diabetes Prediction Using Multinomial Logistic Regression. Aust. J. Basic & Appl. Sci., 8(10): 31-37, 2014
[2] Yang Guo , Guohua Bai , Yan Hu, “Using Bayes Network for Prediction of Type-2 Diabetes”
IEEE Explore Vol. 8, No. 2, July 2012.
[3] S. Peter, An Analytical Study on Early Diagnosis and Classification of Diabetes Mellitus
Bonfring International Journal of Data Mining, Vol. 4, No. 2, June 2014
[4] Tahani Daghistani,Riyad Alshammari, Diagnosis of Diabetes by Applying Data Mining Classification Techniques, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 7, 2016
[5] Huy Nguyen Anh Pham, Evangelos Triantaphyllou, 2008. Prediction of Diabetes by Employing a New Data Mining Approach Which Balances Fitting and Generalization. Computer and Information Science Studies in Computational Intelligence, 131: 11-26.
[6] Mark Hall EibeFrank, Geoffrey Holmes, Bernhard Pfahringer Peter Reutemann, H. Ian Witten, 2009. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1).Shouman, M., T. Urne, R. Stocker, 2012.
[7] G. Karegowda, M.A. Jayaram and A.S. Manjunath,“Cascading K-means Clustering and KNearestNeighbor Classifier for Categorization of Diabetic Patients,” International Journal of Engineering and Advanced Technology (IJEAT), vol.1, no.3, pp. 147-151, 2012.

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Published

2018-08-13

How to Cite

Vishaly M, S., & k, U. (2018). Type 2 Diabetic Prediction Using Machine Learning Algorithm. American Scientific Research Journal for Engineering, Technology, and Sciences, 45(1), 299–307. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/3548

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Articles