Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

G. Parthiban, S. K. Srivatsa Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
Download full text
  1. G Parthiban and S K Srivatsa. Article: Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients. International Journal of Applied Information Systems 3(7):25-30, August 2012. BibTeX

    	author = "G. Parthiban and S. K. Srivatsa",
    	title = "Article: Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 7,
    	pages = "25-30",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"


Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naïve Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics' diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.


  1. Thuraisingham, B. : "A Primer for Understanding and Applying Data Mining", IT Professional, pp: 28-31, 2000
  2. J. Han Kamber, M. 2006. Data Mining: Concepts and Techniques, 2nd ed. San Francisco: Morgan Kaufman.
  3. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From Data Mining to Knowledge Discovery in Databases", AI Magazine, Vol. 17, pp. 37-54, 1996.
  4. World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Available: http://www. who. int/ diabetes/en
  5. World Health Organization. Available: http: // www. who. int/topics/ diabetes mellitus/en/
  6. K. Srinivas et al. / "Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks", International Jounal on Computer Science and Engineering (IJCSE) Vol. . 02, No. 02, 2010, 250-255. Available:http://www. enggjournals. com/ijcse/doc /IJCSE10-02-02-25. pdf
  7. "Hospitalization for Heart Attack, Stroke, or Congestive Heart Failure among Persons with Diabetes", Special report: 2001 – 2003, New Mexico.
  8. World Health Organization. (July 2007-Febuary 2011). [Online]. Available:http://www. who. int/mediacentre/factsheets/fs310. pdf.
  9. Introduction to Data Mining and Knowledge Discovery, Third Edition ISBN: 1-892095-02-5, Two Crows Corporation 10500 Falls Road, Potomac, MD 20854 (U. S. A. ),1999.
  10. L. A. Rose, D. T. , "Discovering Knowledge in Data: An Introduction to Data Mining", ISBN O-471-66657-2, ohn Wiley & Sons, Inc, 2005.
  11. Naïve bayes classifier based on applying bayes theorem: http://en. wikipedia. org/wiki/Naive bayes classifier
  12. Weka Data mining software http://www. cs. waikato . ac. nz/ml/weka
  13. An Introduction to the WEKA Data mining system – http://www. cs. ccsu. edu/~markov/weka-tutorial. pdf
  14. Introduction to Support Vector Machine Available: http://en. wikipedia. org/wiki/Support_vector_machine
  15. Colin Campbell and Yiming Ying, Learning with Support Vector Machines, 2011, Morgan and Claypool. Available: http://www. morganclaypool. com/doi/abs/10. 2200/S00324ED1V01Y201102AIM010?journalCode=aim
  16. H. Barakat, Andrew P. Bradley and Mohammed Nabil H. Barakat (2009) "Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus", IEEE Transactions on Information Technology in Bio Medicine, Volume 14, Issue 4, pp 1-7, 2009. Available: http://ieeexplore. ieee. org /xpls/ abs_ all. jsp?arnumber=5378519 Digital Object Identifier: 10. 1109 /TITB. 2009. 2039485
  17. Mertik M. , Kokol P. , Zalar B. Gaining Features in Medicine Using Various Data-Mining Techniques //Computational Cybernetics ICCC 2005, IEEE rdInternational Conference. – 2005. – P. 21–24.
  18. G. Suganya, D. Dhivya "Extracting Diagnostic rules from SVM" , Journal of Computer Applications (JCA), 2011.
  19. N. Barakat and A. P. Bradley, "Rule Extraction from Support Vector Machines: A Sequential Covering Approach " IEEE Transactions on Knowledge and Data Engineering, Volume 19,no. 6,pp 729-741, 2007. Available: http://ieeexplore. ieee. org/stamp/stamp. jsp?arnumber=04161896 Digital Object Identifier no. 10. 1109/TKDE. 2007. 1023.
  20. S. Balakrishnan, R. Narayanaswamy, N. Savarimuthu, R. Samikannu "SVM Ranking with Backward Search for Feature Selection in Type II Diabetes Databases" 2008 IEEE International Conference on Systems, Man and Cybernetics. Available: http://ieeexplore. ieee. org /xpls /abs_all. jsp?arnumber=4811692 Digital Object Identifier: 10. 1109 /IC SMC. 2008. 4811692
  21. K. E. Heikes, B. Arondekar, D. M. Eddy, and L. Schlessinger, "Diabetes Risk Calculator,A simple tool for detecting undiagnosed diabetes and pre-diabetes," Diabetes Care, vol. 31, no. 5, pp. 1040-1045, 2008
  22. W. Kong, L. Tham, K. Y. Wong, and P. Tan, "Support vector machine approach for cancer detection using amplified fragment length polymorphism (AFLP) method," Proc. the 2nd Asia-Pacific Bioinformatics Conference (APBC2004), Dunedin, New Zealand, 2004.
  23. G. Parthiban, A. Rajesh, S. K. Srivatsa, "Diagnosis of Heart Disease for Diabetic Patients using Naïve Bayes Method", International Journal of Computer Applications (IJCA) Volume 24-No. 3, June 2011, 0975-8887. Available: http://www. ijcaonline. org/archives/volume24/number3/2933-3887 doi 10. 5120/2933-3887
  24. G. Parthiban, A. Rajesh, S. K. Srivatsa, "Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines", International Journal of Computer Applications (IJCA) Volume 48-No. 2, June2012,0975-888. Available: http://www. ijcaonline. org/archives/volume48/number2/7324-0149 doi 10. 5120/7324-0149
  25. SPSS Clementine help file. http//www. spss. com
  26. Kelly H. Zou, PhD; A. James O?Malley, PhD; Laura Mauri, MD, M. Sc "ROC Analysis for Evaluating Diagnostic Test and Predictive Models.


Data Mining, Diabetes, Heart Disease, Machine Learning Methods, Naïve Bayes Method and Support Vector Machines