Research Article
Application of a Mobile Chronic Disease Health-Care System for Hypertension Based on Big Data Platforms
Algorithm 3
C4.5 for important disease rule generation.
Input: | Data partition D: a training set and associated class label C | Attribute list L (selected disease risk factors in previous step) | Output: | Decision tree with its root N | Method: | 1. Create a node N, | 2. if samples has the same class, C then, | 3. return N as leaf node with class C label | 4. if list of attributes is empty then | 5. return N as leaf node with class label that is the most class in training set. | 6. Choose test factor, that has the most GainRatio using attribute_selection_method | 7. give node N with test-attribute label | 8. for each attribute ai in L | 9. add branch in node N to test-attribute=ai | 10. make partition for sample si from training set where test-attribute=ai | 11. if si is empty then | 12. attach leaf node with the most class in training set | 13. else attach node that generated by Gnerate_decision _tree (si, L, test-attribute) | 14. return N |
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