Improve the Accuracy of ID3 Classifier for Heart Disease Prediction

Authors: Ms. Shikha Sharma, Mr. Chetan Chauhan

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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.

Introduction

Data mining is that the non-trivial method of distinguishing valid, novel, doubtless helpful and ultimately intelligible pattern in knowledge. several governmental organization, businesses etc square measure finding some way to gather, store, analyze and report knowledge regarding people ,households or businesses, so as to support (short and long term) coming up with activities. System contains nonpublic or lead like their Social Security variety, financial gain of staff, getting of client etc. Decision tree learning, utilized in statistics, data processing and machine learning, uses a call tree as a prophetical model that maps observations regarding associate degree item to conclusions regarding the item's target price. A lot of descriptive names for such tree models square measure classification trees or regression trees. In these tree structures, leaves represent category labels and branches represent conjunctions of options that cause those category labels. In call analysis, a choice tree may be accustomed visually and expressly represent choices and higher cognitive process. In data processing, a choice tree describes information however not decisions; rather the ensuing classification tree may be associate input for higher cognitive process

Conclusion

In this paper, we have presented a more accurate algorithm for classification. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. In this way accuracy has improved. We also surveyed the existing data classification techniques. We restricted ourselves to the classic classification problem.