Algorithm 1: DCA: Disease Classification Algorithm
Input	:	Disease Training Data (DTrD)
Output	:	Classification Rules (ClassiRule1, ClassiRule2, ClassiRule3)
Step 1	:	R[] <-- Extract all instances from DTrD
Step 2	:	TA <-- Extract target attribute from R
Step 3	:	TAV[] <-- Extract target attribute values from R
Step 4	:	OA[] <-- Extract other attributes from R
Step 5	:	GDA = 0              //Global Disorder Amount
Step 6	:	For each TAVi from TAV
Step 7	:	F <-- Count Frequency of TAVi
Step 8	:	FP <-- F/R.length        //Frequency Probability
Step 9	:	GDA <-- GDA − (FP * log2(FP))
Step 10	:	End For
Step 11	:	ClassiRule1 = ClassificationRuleGeneration(OA,R)
Step 12	:	ClassiRule2 = Classify DTrD based on MLP classifier using weka
Step 13	:	ClassiRule3 = Classify DTrD based on Dl4jMlpClassifier using WekaDeeplearning4j
Step 14	:	Return ClassiRule1, ClassiRule2, ClassiRule3