Table 12.

Comparison of the performance K-Nearest Neighbor classifier (KNN) using different values of k {1, 3, 5, 10} neighbors and using different distance functions; Euclidean distance, Manhattan distance and Minkowski distance using sampling

	Euclidean distance				Manhattan Distance				Minkowski Distance
	K = 1	K = 3	K = 5	K = 10	K = 1	K = 3	K = 5	K = 10	K = 1	K = 3	K = 5	K = 10
Sensitivity	78.43%	65.61%	64.17%	50.00%	78.29%	65.66%	65.68%	61.23%	78.43%	65.61%	64.17%	59.23%
Specificity	96.98%	91.74%	90.53%	89.84%	97.05%	91.80%	90.60%	89.91%	96.98%	91.74%	90.53%	89.84%
Precision	77.18%	33.64%	22.32%	11.50%	77.73%	34.16%	22.94%	16.44%	77.18%	33.64%	22.32%	15.89%
F-score	77.80%	44.47%	33.12%	18.70%	78.01%	44.94%	34.01%	25.91%	77.80%	44.47%	33.12%	25.05%
RMSE	0.23	0.27	0.28	0.29	0.23	0.27	0.28	0.29	0.23	0.27	0.28	0.29
AUC	0.88	0.86	0.85	0.84	0.87	0.86	0.85	0.84	0.87	0.86	0.85	0.84

The results show that the value 1 for the K parameter achieves the highest AUC (0.88) using Euclidean distance