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 a_i in L

9. add branch in node N to test-attribute=a_i

10. make partition for sample s_i from training set where test-attribute=a_i

11. if s_i is empty then

12.  attach leaf node with the most class in training set

13. else attach node that generated by Gnerate_decision _tree (s_i, L, test-attribute)

14. return N