Table 1.
The findings of a meta-analysis of various mechanical ventilators with the suggested model.
Fig 1.
General objective and research flow of the article.
Fig 2.
Modelling of ventilator system.
Table 2.
Modelling parameters for a ventilator system.
Fig 3.
Block diagram of ILC-PID based machine learning.
Fig 4.
Overall flow chart of developed classification framework.
Fig 5.
Five-fold cross-validation strategy.
Fig 6.
Block diagram of ILC-PID controller.
Table 3.
Performance of volume ventilator system machine learning models.
Table 4.
Confusion Matrix Analysis of a ventilator system of volume.
Table 5.
Performance of machine learning models of a ventilator system of pressure.
Fig 7.
Pressure increasing scenario for PID controller.
Fig 8.
Pressure increasing scenario for ILC-PID controller.
Fig 9.
Volume increasing scenario for PID controller.
Fig 10.
Volume increasing scenario for ILC-PID controller.
Fig 11.
Various performance metrics of volume.
Fig 12.
Heat-map analysis of the CPAP for volume.
Fig 13.
Heat-map analysis of the PAV for volume.
Fig 14.
Confusion matrix in CPAP Mode for Volume (a) DT (b) OBT (c) NBT (d) NeNT (e) ET (f) NNT.
Fig 15.
Confusion Matrix in PAV Mode for Volume (a) DT (b) OBT (c) NBT (d) NeNT (e) ET (f) NNT.
Fig 16.
ROC curve for volume in CPAP mode.
Fig 17.
ROC curve for volume in PAV mode.
Fig 18.
Various performance metrics of pressure.
Fig 19.
Heat-map analysis of the CPAP for pressure.
Fig 20.
Heat-map analysis of the PAV for pressure.
Fig 21.
Confusion Matrix in CPAP Mode for Pressure (a) DT (b) OBT (c) NBT (d) NeNT (e) ET (f) NNT.
Table 6.
Confusion Matrix Analysis of a ventilator system of pressure.
Fig 22.
Confusion Matrix in PAV Mode for pressure (a) DT (b) OBT (c) NBT (d) NeNT (e) ET (f) NNT.
Fig 23.
ROC curve for pressure in CPAP mode.
Fig 24.
ROC curve for pressure in PAV mode.