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Fig. 5 | BioMedical Engineering OnLine

Fig. 5

From: Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

Fig. 5

Summary of Experiment 5 (MIL3 + ML: MIL as three feature selector) and Experiment 6 (RFE3 + ML: RFE as a three feature selector)—AUCs for the best oscillometric parameter (BOP), for the best ML algorithms in experiments 5 and 6, and the best ML algorithm with oscillometric parameters (ML7). The figure indicates the best oscillometric parameter and the best ML algorithm in each case. Also, “*” indicates that there a statistically significant difference comparing to BOP (p < 0.05) and “**” (p < 0.01)

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