Genetic Algorithm Based Approach in Attribute Weighting for a Medical Data Set

Table 7

The end result of the genetic algorithm using ONE inference (GA ONE), the attribute weighted -nearest neighbour method with neighbour’s class-based attribute weighting (GA cwk-NN) and with OVA classifiers (GA wk-NN OVA) as evaluation method in population evaluation after at most 20 generations. The true positive rates (TPR) of seven disease classes and the total classification accuracies of the best individual in the end population are given in percentages (%) from 10 times (five times with GA wk-NN OVA) repeated 10-fold cross-validation.

	Disease	ANE	BPV	MEN	SUD	TRA	VNE	BRV	Median TPR	Total accuracy
	Cases	131	173	350	47	73	157	20		951
GA ONE	ONE1	63.5	55.4	90.8	66.2	83.0	68.0	31.5	66.2	73.8
	ONE12	77.0	82.7	96.4	93.6	96.2	76.2	62.0	82.7	87.0
	ONE123	87.6	92.8	98.0	98.5	99.5	84.4	84.5	92.8	93.2
GA cwk-NN	1-NN	70.2	50.0	68.4	30.6	70.0	60.3	15.0	60.3	61.1
	3-NN	70.8	53.9	78.1	27.7	72.5	62.9	14.5	62.9	65.9
	5-NN	70.5	56.1	81.5	23.2	71.9	63.8	12.0	63.8	67.4
	7-NN	69.5	56.6	84.7	21.1	71.0	63.9	8.5	63.9	68.3
	9-NN	69.0	57.5	86.6	18.1	69.7	64.1	6.0	64.1	68.8
GA wk-NN OVA	1-NN	71.5	74.1	84.6	67.2	67.1	77.8	18.0	71.5	76.2
	3-NN	71.6	75.3	91.7	74.9	66.8	78.7	16.0	74.9	79.5
	5-NN	70.4	73.6	92.2	77.0	63.6	79.2	14.0	73.6	79.1
	7-NN	70.4	71.8	92.6	77.0	59.5	79.6	13.0	71.8	78.6
	9-NN	70.5	72.4	92.7	74.9	59.7	79.6	13.0	72.4	78.7