## 4 Discussion  
The study demonstrates that combining traditional risk-scoring methods with machine learning models can enhance the diagnostic accuracy for NF. The choice of the most appropriate model should be guided by the clinical context--whether the goal is to maximize the detection of NF (favoring recall) or to minimize false positives (favoring specificity). These findings have significant clinical implications. The use of machine learning models, particularly the decision tree model, can aid clinicians in making timely and accurate diagnoses of NF, reducing the risk of severe complications.  
The modified LRINEC score that substitutes platelet count for glucose levels reflects the importance of this parameter in the diagnosis of NF. The score assigns points based on the ranges of parameters, with higher scores indicating a greater risk of NF. The inclusion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Levels from Modified Score** & **Necrotizing Fascitis: NF** & **Cellitis** & **Total** \\ \hline Low risk (Score \(\leq\) 5) & 46 & 55 & 101 \\  & 45.5\% & 54.5\% & 100.0\% \\ \hline \multirow{2}{*}{Intermediate risk (score 6–7)} & 22 & 12 & 34 \\  & 64.7\% & 35.3\% & 100.0\% \\ \hline \multirow{2}{*}{High risk (score \(\geq\) 8)} & 6 & 3 & 9 \\  & 66.7\% & 33.3\% & 100.0\% \\ \hline \multirow{2}{*}{Total} & 74 & 70 & 144 \\  & 51.40\% & 48.60\% & 100.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number and percentage of classification of necrotizing fascitis and cellulitis patients using risk level from modified score.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Result** & **Score** \\ \hline \multirow{2}{*}{Creatinine level, mg/dL} & \(\leq\)1.6 & 0 \\  & \(>\)1.6 & 2 \\ \hline \multirow{2}{*}{Hemoglobin, g/dL} & \(>\)13.5 & 0 \\  & 11–13.5 & 1 \\  & \(<\)11 & 2 \\ \hline \multirow{2}{*}{Platelet: PT} & \(<\)189,870 & 0 \\  & 189,870–525,010 & 1 \\  & \(>\)525,010 & 2 \\ \hline Sodium Level & \(\geq\)135 & 0 \\ (mmol/L) & \(<\)135 & 2 \\ \hline \multirow{2}{*}{WBC count} & \(<\)15,000 & 0 \\  & 15,000–25,000 & 1 \\  & \(>\)25,000 & 2 \\ \hline \multirow{2}{*}{C-reactive protein (CRP), (mg/L)} & \(<\)150 (Negative) & 0 \\  & \(\geq\)150 (Positive) & 4 \\ \hline \hline \end{tabular} The maximal scoring is 14, score \(\leq\) 5 = \(<\)50% risk (low); 6–7 = intermediate risk; \(\geq\)8 = \(>\) 75% risk (high).  
\end{table}
Table 5: The modified laboratory risk indicator for NF (LRINEC) score.  
of platelet count as a key variable is particularly noteworthy. The study by [PERSON] et al. [10] highlighted the prognostic value of platelet count in NF patients, and the current study reinforces this by ranking platelet count as the third most important feature. This suggests that platelet count should be considered in the diagnostic process, potentially refining the existing diagnostic tools like the LRINEC score. This modification addresses the data limitations in our hospital, where the glucose and fibrinogen levels needed for calculating the original LRINEC or the MLRINEC score suggested by [PERSON] et al. (2015) [4] are unavailable. These gaps are due to constraints in the laboratory tools used. By refining the score, this study enhances its clinical utility, potentially enabling earlier and more accurate diagnoses of NF.  
Future research could explore the integration of these models into clinical decision support systems, allowing for real-time risk assessment and diagnosis in emergency and inpatient settings. Additionally, further studies could validate the modified LRINEC score in larger, more diverse populations to confirm its generalizability and effectiveness across different healthcare settings.