# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
## 5 Experimental Evaluation
### Numerical Results and Discussion
#### 5.3.2 Comparison of Algorithms to Detect Heart Disease  
The Statlog (Heart) dataset [91] consists of 13 features and 270 samples without no missing values to detect the absence or presence of heart disease. In this dataset 120 of the samples are labeled with the presence of heart disease and 150 samples are labeled with the absence of this disease. The performance of the proposed BSMO with nine variants is assessed and compared with well-known optimizers to diagnose heart disease. The results in Table 4 show that the proposed Threshold-BSMO can obtain a minimum fitness value of 0.1322 and a maximum accuracy of 87.037 than other algorithms.  
\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 & 0.147 & 0.1387 & 85.4815 & 86.2963 & 88.8186 & 94.1537 & 86.8452 & 89.665 & 82.7764 & 86.6325 & 0.1452 & 0.137 \\ BBA & 0.1414 & 0.1380 & 86.0123 & 86.2963 & 94.0096 & 95.4345 & 89.7266 & 91.4855 & 88.5579 & 91.1526 & 0.1959 & 0.1519 \\ BGWO & 0.1383 & 0.1385 & 86.4198 & 86.6667 & 87.4898 & 93.0586 & 85.4259 & 90.2422 & 80.7175 & 87.4738 & 0.1575 & 0.1444 \\ BNOA & 1.4049 & 0.1387 & 86.1728 & 86.2963 & 89.4656 & 91.3609 & 86.9066 & 90.1189 & 89.8787 & 88.087 & 0.1383 & 0.137 \\ S1-BSMO & 0.151 & 0.1432 & 85.1852 & 85.9259 & 89.2216 & 95.26 & 83.6512 & 89.9588 & 78.67 & 87.0474 & 0.1481 & 0.1407 \\ S2-BSMO & 0.146 & 0.1411 & 88.5148 & 86.2963 & 93.66608 & 95.0876 & 99.2841 & 91.4817 & 86.64033 & 87.512 & 0.1964 & 0.1593 \\ S3-BSMO & 0.1481 & 0.1424 & 85.5518 & 89.5299 & 93.3517 & 95.3351 & 89.403 & 91.5718 & 86.2948 & 88.2128 & 0.2015 & 0.1556 \\ S4-BSMO & 0.1495 & 0.1432 & 85.3333 & 85.9259 & 93.1475 & 94.4033 & 98.7136 & 91.6581 & 87.01238 & 93.5831 & 0.1930 & 0.1556 \\ V1-BSMO & 0.1492 & 0.1403 & 85.3704 & 86.2963 & 93.2132 & 95.0297 & 89.4763 & 91.4379 & 86.3764 & 98.294 & 0.1907 & 0.1481 \\ V2-BSMO & 0.1423 & 0.1387 & 85.9383 & 86.2963 & 93.8571 & 96.2621 & 89.3417 & 91.9558 & 89.0497 & 91.2747 & 0.1884 & 0.1593 \\ V3-BSMO & 0.1417 & 0.1380 & 86.037 & 86.2963 & 94.9418 & 96.2525 & 93.3503 & 91.9198 & 88.4579 & 91.1828 & 0.1911 & 0.1481 \\ V4-BSMO & 0.1411 & 0.1351 & 86.0741 & 86.6667 & 94.1042 & 95.6443 & 89.6908 & 91.8579 & 88.5817 & 90.5033 & 0.1956 & 0.1667 \\ Threshold-BSMO & 0.1371 & 0.13222 & 86.5432 & 87.037 & 89.8998 & 93.4192 & 86.7337 & 90.5212 & 82.2366 & 87.3123 & 0.1346 & 0.1296 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Heart disease detection.  
\begin{table}
\begin{tabular}{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** & **Min** \\ \hline ACO & 0.2384 & 0.2318 & 76.5109 & 77.0865 & 85.2345 & 86.6173 & 60.1832 & 64.0414 & 79.9663 & 82.1451 & 0.2351 & 0.2291 \\ BBA & 0.2331 & 0.2281 & 76.9747 & 77.4675 & 86.4089 & 88.748 & 79.8734 & 83.4279 & 59.365 & 63.0096 & 0.23 & 0.2253 \\ bGWO