## 5 Experimental Results  
We performed experiments combining ten different algorithms as classifiers and four different feature-selection strategies (two evolutionary feature-selection methods, one feature selection from the literature, and no feature selection) on the two datasets explained above (With and Without Pathologies). Tables 2 and 3 present the experimental results. These tables contain one row for each configuration, identified by an acronym (_ID_), and 11 additional columns including: the name of the algorithm (_ALGORITHM_), the feature-selection strategy (_FS_), the number of variables of the dataset (_VARIABLES_), the accuracy of the best solution (_BEST_), the accuracy of the worst solution (_WORST_), the mean (_MEAN_), and the standard deviation (_STD_) of 30 runs for the configuration in the row. In addition, the four last columns show precision (_PRECISION_0_ and _PRECISION_1_ )) and recall values (_RECALL_0_ and _RECALL_1_) for class 0 (non-overweight/non-obese) and 1 (overweight or obese) for the best solution with this algorithm. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline
**ID** & **ALGORITHM** & **FS** & **VARIABLES** & **BEST** & **WORST** & **MEAN** & **STD +/—** & **PRECISION_0** & **PRECISION_1** & **RECALL_0** & **RECALL_1** \\ \hline DT-S & Decision Tree & Stud-GA & 14 & 0.7661 & 0.6407 & 0.7150 & 0.0309 & 0.7682 & 0.7639 & 0.7733 & 0.7586 \\ DT-T & Decision Tree & Tournament-GA & 16 & 0.7492 & 0.6542 & 0.7103 & 0.0238 & 0.7255 & 0.7746 & 0.7762 & 0.7237 \\ DT-FFF & Decision Tree & RFCV & 13 & 0.7595 & 0.6373 & 0.6962 & 0.0257 & 0.7597 & 0.7518 & 0.7697 & 0.7413 \\ DT & Decision Tree & No PS & 37 & 0.7254 & 0.6327 & 0.6914 & 0.0232 & 0.7035 & 0.7561 & 0.8013 & 0.6458 \\ XGB-S & XGBOOST & Stud-GA & 20 & 0.7831 & 0.6780 & 0.7216 & 0.0245 & 0.7451 & 0.8239 & 0.8201 & 0.7500 \\ XGB-T & XGBOOST & Tournament-GA & 19 & 0.7599 & 0.6746 & 0.7020 & 0.0187 & 0.7614 & 0.7479 & 0.8171 & 0.6794 \\ XGB-RFUCV & XGBOOST & RFCV & 20 & 0.7424 & 0.6746 & 0.7085 & 0.201 & 0.7677 & 0.7143 & 0.7484 & 0.7353 \\ XGB & XGBOOST & No PS & 37 & 0.7953 & 0.6414 & 0.6981 & 0.0225 & 0.7669 & 0.7500 & 0.7911 & 0.7226 \\
**GR-S** & **Gradient Boosting** & **Stud-GA** & **19** & **0.7966** & **0.6881** & **0.7382** & **0.0231** & **0.8204** & **0.7656** & **0.8204** & **0.7656** \\ GB-T & Gradient Boosting & Tournament-GA & 23 & 0.7684 & 0.6644 & 0.7323 & 0.0251 & 0.7738 & 0.8031 & 0.8387 & 0.7286 \\ CG-RFUCV & Gradient Boosting & RFCV & 17 & 0.7797 & 0.6915 & 0.7324 & 0.0199 & 0.7727 & 0.7899 & 0.8447 & 0.7015 \\ GB & Gradient Boosting & No PS & 37 & 0.7777 & 0.6814 & 0.7305 & 0.0211 & 0.7683 & 0.7393 & 0.8235 & 0.7324 \\ ADB & ADABOOST & No PS & 37 & 0.6746 & 0.6000 & 0.6242 & 0.0189 & 0.6690 & 0.6800 & 0.6690 & 0.6800 \\ BG & BAGGNG & No PS & 37 & 0.7458 & 0.6678 & 0.7114 & 0.0210 & 0.7486 & 0.7411 & 0.8253 & 0.6434 \\ ENB & BERNOULL1 NB & No PS & 37 & 0.7220 & 0.6271 & 0.6675 & 0.0198 & 0.7429 & 0.6917 & 0.7874 & 0.6484 \\ ET & EXTA TREES & No PS & 37 & 0.7627 & 0.6803 & 0.7119 & 0.0276 & 0.7419 & 0.7857 & 0.7931 & 0.7333 \\ GNB & GAUSSAN NB & No PS & 37 & 0.7051 & 0.5932 & 0.6603 & 0.0230 & 0.6792 & 0.7711 & 0.8834 & 0.4848 \\ LR & LOGISTIC REGRESSION & No PS & 37 & 0.7627 & 0.6780 & 0.7098 & 0.0190 & 0.7603 & 0.7651 & 0.7603 & 0.7651 \\ RFC & RANDOM FOREST & No PS & 37 & 0.7763 & 0.6644 & 0.7292 & 0.0240 & 0.7582 & 0.7958 & 0.8000 & 0.7533 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the different algorithms for the set of variables with pathologies. The table shows the algorithm, selection method, number of variables used, best case, worst case, mean, and standard deviation. The algorithm with the highest mean is marked with bold font.