hamed sabbagh gol - Profile on Academia.edu (original) (raw)
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Graduate Center of the City University of New York
National Institute of Technology Karnataka,Surathkal
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Papers by hamed sabbagh gol
Methods: This was an applied descriptive study. UCI standard data and Cleveland data collection w... more Methods: This was an applied descriptive study. UCI standard data and Cleveland data collection were used. The database contains 297 records. Analysis was performed through Weka software and using CRISP3 methodology. The C4.5 decision tree model, using input variables and determining the target variable, was created. Results: According to the applied model, it was found that high levels of cholesterol, sex, age, high maximum heart rate, scan thallium higher than 3 and abnormal ECG have the greatest impact on the risk of coronary heart disease. Furthermore, by using the created decision tree, some rules were extracted that can be used as a model to predict the risk of coronary heart disease. The accuracy of the model created by using decision tree was over 80 percent. Conclusion: According to our calculations, the rate of categorization was 72.6% and the accuracy of C4.5 algorithm was 80.2% that in comparison with the results of studies in the field of data mining of heart diseases, the obtained accuracy for the suggested algorithm is acceptable.
A Detection of Type2 Diabetes using C4.5 Decision Tree
Methods: This was an applied descriptive study. UCI standard data and Cleveland data collection w... more Methods: This was an applied descriptive study. UCI standard data and Cleveland data collection were used. The database contains 297 records. Analysis was performed through Weka software and using CRISP3 methodology. The C4.5 decision tree model, using input variables and determining the target variable, was created. Results: According to the applied model, it was found that high levels of cholesterol, sex, age, high maximum heart rate, scan thallium higher than 3 and abnormal ECG have the greatest impact on the risk of coronary heart disease. Furthermore, by using the created decision tree, some rules were extracted that can be used as a model to predict the risk of coronary heart disease. The accuracy of the model created by using decision tree was over 80 percent. Conclusion: According to our calculations, the rate of categorization was 72.6% and the accuracy of C4.5 algorithm was 80.2% that in comparison with the results of studies in the field of data mining of heart diseases, the obtained accuracy for the suggested algorithm is acceptable.
A Detection of Type2 Diabetes using C4.5 Decision Tree