Table 6.

Performance evaluation results.

Algorithm	1	2	3	4	5	6	7	8	9	10	11	12	13
Random forest	90.76	90.64	90.76	90.53	90.76	90.59	90.70	90.81	90.76	89.36	89.47	88.23	87.62
Adaboost	80.84	80.84	80.84	80.84	80.84	80.84	80.84	80.84	80.84	80.84	80.84	79.27	78.66
Decision tree	86.27	86.27	86.27	86.39	86.33	86.33	85.71	86.44	86.61	62.86	85.32	84.20	83.03

Experiment with attributes removed from the training set. Performance evaluation results, considering the Training set with the ML—Correctly Classified Instances—Accuracy algorithms. Combination of Attributes in the Training Set Algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 Random Forest, Adaboost and Decision Tree. Random Forest showed the best accuracy result (90.81).