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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3531

Advisors: Dr. Million Meshesha
Dr. Adamu Addissie
Keywords: KDD, Data Mining, Decision Tree, Neural Network, Bayesian classifier, Heart Disease
Copyright: Jul-2011
Date Added: 31-Jul-2012
Publisher: AAU
Abstract: Background: Most countries face high and increasing rates of heart disease or Cardiovascular Disease. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve the challenges that face a successful interpretation of echocardiography examination results. Objective: To design a predictive model for heart disease detection using data mining techniques from Transthoracic Echocardiography Report dataset that is capable of enhancing the reliability of heart disease diagnosis using echocardiography. Methodology: Knowledge Discovery in Database (KDD) methodology consisting of nine iterative and interactive steps was adopted to extract significant patterns from a dataset containing 7,339 echocardiography examination reports of patients. The data used for this study was collected by International Cardiovascular Hospital from October, 2008 to March, 2011. Results: The findings of this study revealed all the models built from J48 Decision Tree classifier, Naïve Bayes classifier and Neural Network have high classification accuracy and are generally comparable in predicting heart disease cases. However, comparison that is based on True Positive Rate suggests that the J48 model performs slightly better in predicting heart disease with classification accuracy of 95.56%. Conclusion: This study showed that data mining techniques can be used efficiently to model and predict heart disease cases. The outcome of this study can be used as an assistant tool by cardiologists to help them to make more consistent diagnosis of heart disease.
Description: A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Health Informatics
URI: http://hdl.handle.net/123456789/3531
Appears in:Thesis - Health Informatics

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