## 3 Results
### Statistical Analysis Results  
Our taxonomic analysis of the raw data, assuming the improved taxonomical precision since the bacterial references are constantly changing, resulted in 3603 different bacterial taxonomic units detected. Thus, the gut microbiome consisted of 20 unique phyla, 35 classes, 72 orders, 119 families, and 259 unique genera--additional genus-level data were explored. The taxonomy on the genus level was unavailable for 1506 bacteria (3603/1506; 41.7%). From the remaining bacteria (2097; 58.2%), the most significant genera among the resistant samples belong to the statistically calculated Benjamini-Hochberg \(p\)-value interval from  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Environment** & **ML Algorithms** & **Normalization/Scaling** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Python Scikit-learn & RFC (P1) & Standard Scaler & 0.9 & 1.000 & 0.833 \\ Python Scikit-learn & RFC (P1) & Z-Score Normalizer & 0.9 & 1.0 & 0.75 \\ KNIME & TEL (P1) & Z-Score Normalizer & 0.833 & 0.778 & 1.0 \\ Python Scikit-learn & RFC (P2) & Standard Scaler & 0.917 & 1.000 & 0.833 \\ KNIME & TEL (P2) & Z-Score Normalizer & 0.9 & 1.000 & 0.8 \\ \hline \end{tabular}
\begin{tabular}{c} RFC—Scikit-learn random forest classifier, TEL—Tree ensemble learner, P1—Phase 1 ML modeling, P2—Phase 2 ML modeling. \\ \end{tabular}
\end{table}
Table 2: General ML modeling performance metrics for the resistant and non-resistant CRC postoperative individuals’ group.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \hline
**Environments and ML** & **Resistant** & **Non-Resistant** & **Resistant** & **Resistant** & **Non-Resistant** \\ \hline Python Scikit-learn—RFC (P1) & 0.83 & 1.00 & 1.00 & 0.80 & 0.91 & 0.89 \\ Python Scikit-learn—RFC (P1) & 0.75 & 1.00 & 1.00 & 0.86 & 0.86 & 0.92 \\ KNIME—TEL (P1) & 1 & 0.778 & 0.600 & 1.000 & 0.750 & 0.875 \\ Python Scikit-learn—RFC (P2) & 0.83 & 1.00 & 1.00 & 0.86 & 0.91 & 0.92 \\ KNIME—TEL (P2) & 0.800 & 1.000 & 1.000 & 0.833 & 0.889 & 0.909 \\ \hline \end{tabular}
\begin{tabular}{c} RFC—Scikit-learn random forest classifier, TEL—Tree ensemble learner, P1—Phase 1 ML Modeling, P2—Phase 2 ML modeling. \\ \end{tabular}
\end{table}
Table 3: Detailed ML modeling performance metrics for the resistant and non-resistant CRC postoperative individuals’ group.  
0.009 to 0.024. Thus, in the resistant group, we found the _Bacteroides_ (0.009), followed by _Lachnoclostridium_ (0.017), _Streptococcus_ (0.021), _Eggerthella_ (0.024), _Escherichia-Shigella_ (0.026), _Flavourfract_ (0.04), and _[Ruminococcus] torques group_ (0.044). Accordingly, the most significant genera among the non-resistant samples belong to Benjamini-Hochberg \(p\)-value interval from 0.001 to 0.047. In the non-resistant group we found the _Ruminococcus_ (0.002), _Oscillospiraceae-UCG-002_ (0.003), _Oscillospiraceae NK4A214 group_ (0.010), _Lachnospiraceae FCS020 group_ (0.019), _Desulfovibrio_ (0.012), _Intestinibacter_ (0.038), _Christensenel-laceae R-7 group_ (0.047), _Clostridium sensu stricto 1_ (0.016), _Lachnospiraceae NC2004 group_ (0.037), _Oscillospiraceae-UCG-005_ (0.014), _Blautia_ (0.045), and _Alistipes_ (0.033). The statistical analysis results for genera abundances in resistant and non-resistant groups are presented in Figure 4.