# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
## 5 Experimental Evaluation
### Numerical Results and Discussion
#### 5.3.4 Comparison of Algorithms to Detect Coronavirus Disease 2019 (COVID-19)  
The COVID-19 pandemic is an infectious disease of severe acute respiratory syndrome Coronavirus 2019 [99] which was initiated in Wuhan, China, in December 2019 and profoundly affected human life [100]. Early detection of Coronavirus disease can reduce the transmission rate and slow the epidemic outbreak. Many optimization algorithms have been developed to alleviate this global crisis [101]. In this section, the performance of the proposed algorithm is evaluated in the Coronavirus disease 2019 (COVID-19) dataset [92]. This dataset consists of two classes, death or recovery, and 13 features, including location, country, gender, age, whether the patients visited Wuhan, whether the patients from Wuhan had fever, cough, cold, fatigue, body pain, malaise, and day's difference between the symptoms being noticed and admission to the hospital. The results reported in Table 6 indicate the proposed Threshold-BSMO outperforms all contender algorithms and BSMO variants to detect COVID-19. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**Fitness**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**Error**} \\ \cline{2-13}  & **Avg** & **Min** & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Min** \\ \hline ACO & 0.0521 & 0.0493 & 95.2805 & 95.4825 & 98.3325 & 99.0601 & 96.3844 & 97.4589 & 74.0994 & 78.5774 & 0.0477 & 0.0452 \\ BBA & 0.0508 & 0.0494 & 95.3575 & 95.4838 & 98.411 & 98.9281 & 96.5039 & 97.1542 & 74.7778 & 79.4731 & 0.0464 & 0.0452 \\ BGVO & 0.0482 & 0.0455 & 95.4915 & 95.7137 & 98.6061 & 99.3678 & 96.1273 & 97.5426 & 73.3577 & 80.9149 & 0.0451 & 0.0429 \\ BWOA & 0.0518 & 0.0493 & 95.2667 & 95.7164 & 98.3045 & 99.0229 & 96.4615 & 97.1998 & 74.626 & 82.0579 & 0.0479 & 0.0428 \\ SI-BSMO & 0.0515 & 0.0493 & 95.417 & 95.5988 & 99.2906 & 99.7496 & 97.7266 & 98.2616 & 83.0173 & 87.3208 & 0.0511 & 0.0452 \\ S2-BSMO & 0.0516 & 0.049 & 95.3861 & 95.5961 & 99.3947 & 100 & 97.5923 & 97.9672 & 82.1743 & 85.5629 & 0.052 & 0.0498 \\ S3-BSMO & 0.0517 & 0.0497 & 95.3308 & 95.6001 & 99.3703 & 100 & 97.5576 & 98.2839 & 81.7173 & 87.2988 & 0.0521 & 0.0487 \\ S4-BSMO & 0.0516 & 0.049 & 95.3347 & 95.5948 & 99.4093 & 100 & 97.5954 & 98.126 & 82.1407 & 86.2074 & 0.0532 & 0.0498 \\ VI-BSMO & 0.051 & 0.0497 & 95.2469 & 95.5974 & 99.5898 & 100 & 97.3384 & 97.924 & 80.376 & 84.761 & 0.0537 & 0.0476 \\ V2-BSMO & 0.0509 & 0.0489 & 95.2663 & 95.4812 & 99.8182 & 100 & 97.364 & 97.8237 & 80.6954 & 84.0749 & 0.053 & 0.0474 \\ V3-BSMO & 0.051 & 0.0486 & 95.2695 & 95.4838 & 99.7693 & 100 & 97.3319 & 97.9259 & 80.477 & 83.9283 & 0.053 & 0.0475 \\ V4-BSMO & 0.0506 & 0.0478 & 95.2692 & 95.4892 & 99.7845 & 100 & 97.4058 & 97.956 & 80.7991 & 84.4487 & 0.0532 & 0.0452 \\ Threshold-BSMO & 0.0488 & 0.0451 & 95.537 & 95.8353 & 99.3774 & 100 & 97.7178 & 98.0502 & 83.1011 & 87.2075 & 0.0518 & 0.0487 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coronavirus disease 2019 (COVID-19) detection.