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Prediction Heart Disease Using Naive Bayesian Classification

http://hdl.handle.net/20.500.12678/0000004086
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bc0b97e6-d51b-4346-bc87-3c15ae70e1ea
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55198.pdf 55198.pdf (270 Kb)
Publication type
Article
Upload type
Publication
Title
Title Prediction Heart Disease Using Naive Bayesian Classification
Language en
Publication date 2009-12-30
Authors
Aye, Khin Myo
Yuzana
Description
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, rarely true in real-world applications. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not mined to discover of hidden information for effective decision making. Advanced data mining techniques can help medication. In this system, we developed a prototype that is Prediction Heart Disease Using Naive Bayesian Classification. We exploited medical profiles such as age, gender, blood pressure and blood sugar , it can predict the likelihood of patients getting a heart disease. This system is computer-based, user-friendly interface and the accuracy are reliable and expandable Moreover, we tested the train data of 326 and test data of 177 records and measured the performance with sensitivity and specificity So, the experimental result shows that the accuracy got 91.21%.
Keywords
Naive Bayesian Classifier, Diagnosis of Heart Disease, Probability, Accuracy, Holdout Method
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1790
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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