Fig. 5. Reducing the false positives (FPs) using decision thresholding. (a) Schematic illustrating the application of decision thresholding for cancer cell classification; (b) selection of optimal threshold using receiver operator characteristic (ROC) analysis. A stringent threshold (α) of 0.9999999 is selected as it yielded the minimum FPR without any further change; (c) to demonstrate the applicability of the selected threshold in reducing the FPR, four artificially mixed samples were generated in silico. In these datasets, the number of MCF-7 cancer cells (N_MCF−7) was kept constant at 100, while the number of WBCs (N_WBC) was varied from 1000–10 000. It was ensured that no overlap between the two cell types existed across the four datasets. For increasing WBC counts, the FPR is plotted for 4 values of α: 0.5 (red), 0.9 (green), 0.999 (black) and 0.9999999 (blue). Error bars indicate the standard deviation of three trials (n = 3); (d) effect of α on the TPR is shown. In these trials, three datasets were generated, wherein, N_WBC was fixed at 5000 and N_MCF−7 was varied from 1–100. Error bars indicate the standard deviation of three trials (n = 3).