Mining Echocardiography Data to Predict Heart Disease (Medphd)
| dc.contributor.advisor | Meshesha, Million (PhD) | |
| dc.contributor.author | Habte, Thomas | |
| dc.date.accessioned | 2018-12-04T08:20:21Z | |
| dc.date.accessioned | 2023-11-29T04:57:02Z | |
| dc.date.available | 2018-12-04T08:20:21Z | |
| dc.date.available | 2023-11-29T04:57:02Z | |
| dc.date.issued | 2012-06 | |
| dc.description.abstract | Background: These days, a major challenge of health care is reaching to correct diagnosis of specific disease condition. Poor clinical decision leads to catastrophic consequences which are unacceptable. Decision making process at the health care setting needs to be supported with more advanced technology including a computer based information system. Objective: This study aims at extracting hidden knowledge (patterns and relationships) associated with echocardiography datasets and designing a predictive model for heart disease detection using data mining techniques. Methods: A Hybrid Data Mining methodology is followed, which is a six-step knowledge discovery process. The data for this research obtained from International Cardiovascular Hospital in Addis Abeba, Ethiopia. This study investigates the use of different data mining techniques, Decision tree, neural network, and support vector machine for classification tasks. On Transthoracic Echocardiography report datasets, descriptive data summarization and visualization were taken to gain understanding of the data. Moreover, missing values, outliers data, data integration and transformation were managed at preprocess stage of hybrid process model. Results: The results show that all the models performed well, though J48 Decision tree algorithms outperforms support vector machine, Multilayer Perceptron Neural Network, registering 96.73%. The best attributes selected by J48 decision tree are Left Atrium Systole Diameter, LV ejection fraction, and Tricuspid velocity. As per discussion made with the cardiologist, one of the interesting rule, a patient with Left atrium systole diameter less than or equal to 40 millimeter and LV ejection fraction less than or equal to 51% blood pumped out of ventricles and Tricuspid velocity is greater than 2.5 centimeter per second results Left Ventricle dysfunction and Pulmonary hypertensive disorder. Conclusion: The result thus obtained in this study is promising to apply data mining for heart disease detection. To make usable the knowledge extracted in this study, an attempt has made to design a knowledge-based system that shows the potential to integration. It is a further research direction.Keywords: Echocardiography, Knowledge discovery process, Decision tree, neural network, Support vector machine | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/14800 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Echocardiography | en_US |
| dc.subject | Knowledge discovery process | en_US |
| dc.subject | Decision tree | en_US |
| dc.subject | neural network | en_US |
| dc.subject | Support vector machine | en_US |
| dc.title | Mining Echocardiography Data to Predict Heart Disease (Medphd) | en_US |
| dc.type | Thesis | en_US |