Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

Table 6 Performance of classification algorithms.

Algorithm^f	Accuracy^a	Recall^b	Precision^c	Specificity^d	NPV^e
ADID + LLM	80%	90%	82%	57%	72%
Decision tree	63%	76%	72%	35%	40%
PAM	81%	90%	83%	61%	74%
SVM	84%	94%	84%	61%	82%

^a Accuracy is the fraction of correctly classified patients and overall classified patients.
^b Recall is the fraction of correctly classified good outcome patients and the overall predicted good outcome patients.
^c Precision is the fraction of correctly classified good outcome patients and the predicted good outcome patients.
^d Specificity is the fraction of correctly classified poor outcome patients and the overall poor outcome patients.
^e NPV(negative predictive value) is the fraction of correctly classified poor outcome patients and the overall predicted poor outcome patients.
^f Machine learning algorithms utilized for comparison.

ISSN: 1471-2105