CFP last date
20 January 2025
Reseach Article

Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2

Published on November 2012 by Abid Sarwar, Vinod Sharma
Issues and Challenges in Networking, Intelligence and Computing Technologies
Foundation of Computer Science USA
ICNICT - Number 3
November 2012
Authors: Abid Sarwar, Vinod Sharma
5b0a41a6-fef2-4b4c-a27b-c413adc7323c

Abid Sarwar, Vinod Sharma . Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2. Issues and Challenges in Networking, Intelligence and Computing Technologies. ICNICT, 3 (November 2012), 14-16.

@article{
author = { Abid Sarwar, Vinod Sharma },
title = { Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2 },
journal = { Issues and Challenges in Networking, Intelligence and Computing Technologies },
issue_date = { November 2012 },
volume = { ICNICT },
number = { 3 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 14-16 },
numpages = 3,
url = { /specialissues/icnict/number3/9030-1043/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Issues and Challenges in Networking, Intelligence and Computing Technologies
%A Abid Sarwar
%A Vinod Sharma
%T Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2
%J Issues and Challenges in Networking, Intelligence and Computing Technologies
%@ 0975-8887
%V ICNICT
%N 3
%P 14-16
%D 2012
%I International Journal of Computer Applications
Abstract

Artificial Intelligence is now a days gaining immense importance and is becoming a key technology in many fields ranging from banking industry, to travel industry, to communication industry, and to robotic industry. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc. . . The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is inability of body to manage the levels of sugar in the blood. It being one of the most chronic diseases around the world causes around 3. 8 million deaths every year. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the naïve-Bayes classification algorithm to serve the purpose. When the parameters of the test data are fed to the system, it anticipates & classifies the test data into one of the two categories viz diabetic & not diabetic. The performance of AI method when compared with the medical diagnosis system was found to be 95%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results.

References
  1. Harris M, Zimmet P. Classification of diabetes mellitus and other categories of glucose intolerance. In Alberti K, Zimmet P, Defronzo R, editors. International Textbook of Diabetes Mellitus. Second Edition. Chichester: John Wiley and Sons Ltd; 1997. p9-23
  2. Williams textbook of endocrinology (12th ed. ). Philadelphia: Elsevier/Saunders. pp. 1371–1435. ISBN 978-1437703245.
  3. Optimal naïve Bayes harry zahang
  4. An empirical comparison of supervised learning algos
  5. The use of artificial neural networks in decision support in cancer. Paulo J. Lisboa & Azzam F. G. Taktak
  6. Artificial Intelligence in Predicting Bladder Cancer Outcome: A Comparison of Neuro-Fuzzy Modeling and Artificial Neural Networks, James W. F. Catto, Derek A. Linkens, Maysam F. Abbod, Minyou Chen, Julian L. Burton, Kenneth M. Feeley, and Freddie C. Hamdy2
  7. Artificial neural networks for decision-making in urologic oncology, Anagnostou T, Remzi M, Lykourinas M, Djavan B
  8. Bratko I. , Kononenko I, Learning Rules from Incomplete and Noisy Data, in B Phelps (ed. ) Interactions in Arti_cial Intelligence and Statistical Methods, Hampshire, Technical Press.
  9. Experiments in automatic learning of medical diagnostic rules international school for synthesis of Expert knowledge Workshop, Bled, August 1981. Kononenko I, Bratko I, Roskar E.
  10. Lesmo. L. (et al) Learning of fuzzy production riles for medical diagnosis, In Gupta. M. M & Sanchez E. (eds), approximate reasoning in desion analysis, North-Holland 1982
  11. Hojker S, Kononenko I, Juka A, Fidler V. & Porenta. M, Expert System's Development in Management of Thyroid Disease, proc. European Congress for nuclear medicine, Milano, Sept. , 1988
  12. Horn K. A, Compton P. Lazarus L. , Quinlan J. R, an Expert System for Interpretation of Thyroid Assays in Clinical Laboratory, the Australian Computer Journal Vol. 17, No. 1, 1985, pp 7-11.
  13. Hepatitis disease diagnosis using Back-propagation and Naïve Bayes classifier.
  14. Machine learning for Medical Diagnosis: History, State of Art and Perspective; igor Knononkp…………
  15. Bratko I. , Mozetic I. , Lavrac N. , KARDIO: A study in deep and qualitative knowledge for expert system Cambridge,MA:MIT Press,1989.
  16. Catlett J. , On changing continuous attributes into ordered discrete attributes, proc. European Working Session on Learning-91, Proto ,March 4-6 1991, pp. 164-178.
  17. Clark p . & Boswell R. , Rule Induction with CN2: Some Recent Improvements, Proc. Europea Working Session on Learning-91,Porto, Portugal, March, 1991, pp. 151-163.
  18. Nunez M. , Decision Tree Induction Using Domain Knowledge, In: Wielinga B. et al. (eds. ) Current Trends in Knowledge Acquisition, Amsterdam: IOS Press, 1990.
  19. Muggleton S. , Inductive Acquisition of Knowledge, Turing Institute press & Addison-wesley, 1990.
  20. Kern j. , Dezelic G. , Tezak-Bencic M. , Durrigl T. , Medical Decision Making Using Inductive Learning program (in Croatian), Proc 1st Congress on Yougoslav Medical Informatics, Beogard, Dec 6-8, 1990, pp. 221-228.
  21. The Optimality of Naïve Bayes, Harry Zhang. 2004, American Association for Artificial Intelligence.
  22. Comparative study of Naïve Bayes and KNN for Tuberculosis; Hardik Maniya , Mohsin Hasan, Komal Patil.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Data Mining Machine Learning Diabetes Naive Bayes Classifier Medical Diagnosis