Improve the Accuracy of ID3 Classifier for Heart Disease Prediction

Authors: Shikha Sharma, Chetan Chauhan

Published in: IJRRETAS, Volume 3, Issue 5, pp. 1-5 (01 May 2017)

Impact Factor: 3.5 (2017)

License: CC BY 4.0 CC BY 4.0 (Open Access)

Certificate: View Certificate

Abstract

Data Classification may be a very fashionable and computationally overpriced task. Most of those information classification techniques square measure supported the conception of call trees. several researchers have worked on the malady prediction systems exploitation the information mining techniques. a number of the systems square measure for predicting one malady and a few for the predicting the multiple diseases. Still there\'s scope to enhance the potency of the malady prediction. during this paper, we tend to square measure presenting associate degree updated ID3 rule. a brand new attribute choice rule has projected during this paper. The accuracy of the projected methodology is best than the present rule.

Keywords
  • data processing
  • classification rule
  • ID3 rule
  • call tree
How to Cite
APA: Sharma, S., & Chauhan, C. (2017). Improve the Accuracy of ID3 Classifier for Heart Disease Prediction. International Journal for Rapid Research in Engineering Technology & Applied Science, 3(5), 1–5. https://ijrretas.com/research-paper/improve-the-accuracy-of-id3-classifier-for-heart-disease-prediction-
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