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. Author manuscript; available in PMC: 2023 Sep 12.
Published in final edited form as: Nano Lett. 2022 Mar 29;22(9):3620–3627. doi: 10.1021/acs.nanolett.1c04722

Figure 4: Virus detection on FEIMA using supervised machine learning.

Figure 4:

(A) Schematic of the random forest algorithm. (B) Classification accuracy for control, SARS-CoV-2, H1N1 A, Marburg, and Zika sample using multiclass random forest classifier. The black box represents the median value. (C) Confusion matrix showing the percentage of a sample getting classified into various classes. (D) Classification accuracy using binary random forest classification for SARS-CoV-2 and H1N1 A (respiratory) samples in one class and the other viruses (Zika and Marburg) in the non-respiratory class. (E) ROC curve for the performance of the binary classifications.