Skip to main content

Table 1 Comparison of performance accuracies, as percentage of samples correctly classified, for classification of canine articular cartilage data for seven different machine-learning methods, using leave-one-out cross-validation

From: Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Dataseter Naive Bayes SVM k-nearest neighbour JRip (rule based) Random forest C4.5 BioHEL
ProteinProphet 39.1 52.2 34.8 43.5 34.8 52.2 73.9
emPAI 52.2 56.5 52.2 78.3 39.1 73.9 56.5
ProteinProphet and emPAI combined 52.2 52.2 43.5 78.3 26.1 47.8 78.3
  1. For the ‘ProteinProphet and emPAI combined’ the two scores were combined into one dataset. The highest accuracies achieved in each dataset are shown in bold.