## 3 Results
### Quantitative Bone Marrow Assessment
#### 3.2.2 Machine Learning Models
\begin{table}
\begin{tabular}{c c c c c c|c c c c|c c c} \hline \hline \multirow{2}{*}{**F1\_weighted**} & \multicolumn{4}{c}{**NHL**} & \multicolumn{4}{c}{**FL**} & \multicolumn{4}{c}{**DLBCL**} \\ \cline{2-13}  & **ORIG** & **RED** & **OS** & **OS\_R** & **ORIG** & **RED** & **OS** & **OS\_R** & **ORIG** & **RED** & **OS** & **OS\_R** \\ \hline \multirow{9}{*}{**PET**} & F1\_0 & **0.918** & 0.852 & **0.918** & 0.873 & 0.903 & **1.000** & 0.933 & 0.968 & 0.897 & 0.889 & 0.897 & **0.929** \\  & F1\_1 & 0.800 & **0.842** & 0.824 & 0.700 & 0.727 & **0.923** & 0.667 & 0.833 & **0.500** & **0.500** & 0.400 & **0.500** \\  & F1\_2 & 0.333 & 0.222 & 0.500 & **0.571** & 0.000 & 0.000 & 0.000 & 0.000 & 0.667 & 0.400 & 0.000 & 0.500 \\  & F1\_m & 0.684 & 0.639 & **0.747** & 0.715 & 0.543 & **0.641** & 0.533 & 0.600 & **0.688** & 0.596 & 0.432 & 0.643 \\  & F1\_m * & 0.786 & 0.748 & **0.822** & 0.792 & 0.710 & **0.812** & 0.714 & 0.774 & 0.757 & 0.716 & 0.619 & **0.759** \\  & F1\_w & 0.834 & 0.792 & **0.843** & 0.803 & 0.815 & **0.962** & 0.842 & 0.909 & 0.741 & 0.754 & 0.691 & **0.807** \\ \hline \multirow{9}{*}{**MFC**} & F1\_0 & 0.722 & & **0.743** & & 0.720 & 0.500 & **0.727** & 0.545 & _0.889_ & & 0.842 & \\  & F1\_1 & **0.593** & & 0.538 & & 0.533 & 0.375 & **0.625** & 0.444 & 0.000 & & **0.400** & \\  & F1\_2 & 0.000 & & 0.000 & & 0.000 & **0.333** & 0.000 & 0.000 & **0.500** & & _0.500_ & \\  & F1\_m & **0.438** & & 0.427 & & 0.418 & 0.403 & **0.451** & 0.330 & 0.463 & & _0.581_ & \\  & F1\_m * & **0.609** & & 0.601 & & 0.589 & 0.543 & **0.620** & 0.504 & 0.652 & & **0.700** & \\  & F1\_w & 0.663 & & **0.667** & & 0.631 & 0.521 & **0.683** & 0.515 & _0.748_ & & 0.728 & \\ \hline \multirow{9}{*}{**pGT**} & F1\_0 & 0.723 & 0.698 & **0.846** & 0.698 & 0.727 & 0.750 & 0.727 & **0.737** & **0.833** & 0.667 & 0.783 & 0.700 \\  & F1\_1 & 0.690 & **0.727** & 0.720 & 0.710 & **0.750** & 0.667 & 0.696 & 0.720 & 0.500 & **0.571** & 0.500 & 0.500 \\ \cline{1-1}  & F1\_2 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.500 & **0.667** & 0.444 & 0.286 & 0.600 \\ \cline{1-1}  & F1\_m & 0.471 & 0.475 & **0.522** & 0.469 & 0.492 & 0.472 & 0.474 & **0.652** & **0.667** & 0.561 & 0.523 & 0.600 \\ \cline{1-1}  & F1\_m * & 0.632 & 0.639 & **0.686** & 0.632 & 0.659 & 0.640 & 0.641 & **0.740** & **0.744** & 0.666 & 0.644 & 0.690 \\ \cline{1-1}  & F1\_w & 0.679 & 0.686 & **0.754** & 0.682 & 0.727 & 0.706 & 0.707 & **0.745** & **0.732** & 0.657 & 0.654 & 0.666 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metrics for the models with the best performance (maximum F1_w) for each group (ORIG, OS, RED, or OS_R), group of patients (NHL, FL, and DLBCL), and labeling (PET, MFC, and pGT). **(F1_0)** F1_score for BMI\(-\) class; **(F1_1)** F1_score for BMI\(+\) class; **(F1_2)** F1_score for rBM class; **(F1_m)** F1_score_macro, calculated as the mean F1_score of the three previous metrics; **(F1_m *)** F1_score_macro*, calculated as the mean F1_score considered as binary problem for any class; **(F1_w)** F1_score_weighted. Grey cells represent the lack of those groups, i.e., no significant features after reduction. Green cells represent crossover between the model with the best F1_m* and the best F1_w. The measures marked in bold are the best of its metric for the pair labeling and group of patients; those marked in italics are the best of its labeling, i.e., the best of its column; and the underline indicates the best for its group of patients. improves in those models, obtaining good classification of the BMI\(-\) and BMI\(+\), although no rBM cases are detected. Table 6 shows the model and BW corresponding to the models whose performance is shown in Tables 4 and 5. Regarding the selected machine learning model, there is a notable difference between some models that result in the best performance in multiple cases, such as RF, MLP1, GB, or LR (best models in 17, 13, 11, and 11 cases, respectively), compared to others that obtain the best result in only one combination (RBF, SIG, NB, or KNN).