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Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 95 - Number 17
Year of Publication: 2014
Veena Vijayan V
Aswathy Ravikumar

Veena Vijayan V and Aswathy Ravikumar. Article: Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus. International Journal of Computer Applications 95(17):12-16, June 2014. Full text available. BibTeX

	author = {Veena Vijayan V and Aswathy Ravikumar},
	title = {Article: Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {95},
	number = {17},
	pages = {12-16},
	month = {June},
	note = {Full text available}


Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various available traditional methods for diagnosing diabetes are based on physical and chemical tests. These methods can have errors due to different uncertainties. A number of Data mining algorithms were designed to overcome these uncertainties. Among these algorithms, amalgam KNN and ANFIS provides higher classification accuracy than the existing approaches. The main data mining algorithms discussed in this paper are EM algorithm, KNN algorithm, K-means algorithm, amalgam KNN algorithm and ANFIS algorithm. EM algorithm is the expectation-maximization algorithm used for sampling, to determine and maximize the expectation in successive iteration cycles. KNN algorithm is used for classifying the objects and used to predict the labels based on some closest training examples in the feature space. K means algorithm follows partitioning methods based on some input parameters on the datasets of n objects. Amalgam combines both the features of KNN and K means with some additional processing. ANFIS is the Adaptive Neuro Fuzzy Inference System which combines the features of adaptive neural network and Fuzzy Inference System. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases.


  • C. kalaiselvi,G. m. Nasira,2014. "A New Approach of Diagnosis of Diabetes and Prediction of Cancer using ANFIS",IEEE Computing and Communicating Technologies,pp 188-190
  • Velu C. M, K. R. Kashwan,2013. "Visual Data Mining Techniques forClassification of Diabetic Patients", IEEE International Advance Computing Conference (IACC),pp-1070-1075.
  • Sapna. S,Tamilarasi. A and Pravin Kumar. M, 2012 "Implementation of genetic algorithm in predicting diabetes", IJCSI, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 4, pp. 393-398
  • Nirmala Devi M. ,Appavu alias Balamurugan S. ,Swathi U. V. , 2013. ",An amalgam KNN to predict Diabetes Mellitus", IEEE International Conference on Emerging Trends in Computing ,Communication and Nanotechnology(ICECCN),pp 691-695
  • Asha Gowda Karegowda and Jayaram. A. M. , 2009"Cascading GA & CFS for feature subset selection in medical data mining", IEEE International Advance Computing Conference, Patiyala, India
  • Krzysztof J. Cios, G. William Moore (2002) 'Uniqueness of Medical Data Mining', Artificial Intelligence in Medicine Journal pp 1-19.
  • Asha Gowda Karegowda , A. S. Manjunath , M. A. Jayaram (2011) "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 Krzysztof J. Cios, G. William Moore (2002) 'Uniqueness of Medical Data Mining' Artificial Intelligence in Medicine Journal pp 1-19
  • Siti Farhanah Bt Jaafar and Dannawaty Mohd Ali,"Diabetes mellitus forecast using artificial neural networks", Asian conference of paramedical research proceedings, 5-7,September, 2005, Kuala Lumpur, Malaysia.
  • T. Jayalakshmi and Dr. A. Santhakumaran, "A novel classificationmethod for classification of diabetes mellitus using artificial neural networks". 2010 International Conference on Data Storage and Data engineering
  • Edgar Teufel1, Marco Kletting1, Werner G. Teich2,Hans- Jorg Pfleiderer1, and Cristina Tarin-Sauer3,sept. 2003"Modelling the Glucose Metabolism with Backpropagation Through Time Trained Elman Nets", IEEE 13th Workshop on Neural Networks for SignalProcessing, NNSP'03, pp. 789 – 798
  • Fuluf helo V Nelwamondo, Shakir Mohammed andTshilidzi Mawala, 2007 "Missing Data: A comparison ofneural network and expectation maximization techniques", Current Science, Vol 93, No 11
  • J. Prather,1997 et al. , "Medical data mining: knowledgediscovery in a clinical data warehouse. ," Proc AMIAAnnu Fall Symp, pp. 101–105.
  • R. Bellazzi and B. Zupan, 2008. "Predictive data mining inclinical medicine: Current issues and guidelines,"International Journal of Medical Informatics, vol. 77, pp. 81-97
  • UCI machine learning repository and archive. ics. uci. edu/ml/datasets. html