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Table 2 Performance comparison among MSpectraAI and other diverse types of machine-learning algorithms on different datasets

From: MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks

  Oral cancer dataset Head-and-neck dataset
Random Forest Logistic Regression Linear SVM RBF SVM MSpectraAI Linear SVM MSpectraAI
Accuracy 0.674 0.594 0.573 0.540 0.90 0.868 1.00
Sensitivity 0.751 0.811 0.809 0.799 1.00 0.85 1.00
Precision 0.753 0.654 0.638 0.615 0.833 0.889 1.00
F1 0.751 0.724 0.713 0.695 0.909 0.895 1.00