## 3 Results
### Machine Learning in Classifying Necotizing Fascitis from Cellulitis Patients
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Independent** & **Dependent** & **Range** & **Mean** & **SD** & **Normal Value** \\ \cline{2-6}
**Variables** & **Variables** & **Range** & **Mean** & **SD** & **Ranges** \\ \hline \multirow{3}{*}{CT: creatinine (mg/dL)} & All & 0.4–13.5 & 1.71 & 2.58 & \multirow{3}{*}{male: 0.6–1.2 female: 0.5–1.1 female: 0.5–1.1} \\ \cline{2-6}  & NF & 0.4–12.4 & 1.49 & 1.97 & \\ \cline{2-6}  & Non-NF & 0.4–13.5 & 1.95 & 3.07 & \\ \hline \multirow{3}{*}{HB: hemoglobin (g/dL)} & All & 3.8–15.8 & 10.32 & 2.26 & \multirow{3}{*}{male: 14–18 female: 12–16 female: 12–16} \\ \cline{2-6}  & NF & 4.9–14.6 & 9.80 & 1.73 & \\ \cline{2-6}  & Non-NF & 3.8–15.8 & 10.88 & 2.60 & \\ \hline \multirow{3}{*}{PT: platelet count (cells/mm\({}^{3}\))} & All & 14,000–842,000 & 357,444.44 & 166,993.21 & \multirow{3}{*}{150,000–440,000} \\ \cline{2-6}  & NF & 14,000–842,000 & 394,148.65 & 180,543.75 & \\ \cline{2-6}  & Non-NF & 45,000–786,000 & 318,642.86 & 141,345.58 & \\ \hline \multirow{3}{*}{NA: sodium (mmol/L)} & All & 49–143 & 135.08 & 8.45 & \multirow{3}{*}{136–144} \\ \cline{2-6}  & NF & 49–143 & 134.00 & 10.92 & \\ \cline{2-6}  & Non-NF & 122–143 & 136.23 & 4.27 & \\ \hline WBC: white blood & All & 3610–41,540 & 12,899.25 & 7578.10 & \multirow{3}{*}{4500–10,000} \\ \cline{2-6}  & NF & 4370–41,540 & 13,162.03 & 6308.06 & \\ \cline{2-6}  & Non-NF & 3610–27,210 & 10,780.57 & 4812.59 & \\ \hline \multirow{3}{*}{CRP: C-reactive protein (mg/L)} & All & Negative = 70, Positive = 74 & - & \\ \cline{2-6}  & NF & Negative = 36, Positive = 38 & - & \textless{}10\) mg/L \\ \cline{2-6}  & Non-NF & Negative = 34, Positive = 36 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary statistics of feature variables. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Predicted** & **Actual** & \\ \cline{2-4}  & **Predicted** & **Positive** & **Negative** \\  & **(Necrotizing Fascitis: NF)** & **(Cellulitis)** \\ \hline \multirow{3}{*}{LR: Logistic Regression} & Positive & \multirow{3}{*}{5} & \multirow{3}{*}{1} \\ \cline{2-4}  & (Necrotizing Fascitis: NF) & & \\ \cline{2-4}  & Negative (Cellulitis) & & \\ \hline \multirow{3}{*}{DT: Decision Tree Classifier} & Positive & \multirow{3}{*}{9} & \multirow{3}{*}{3} \\ \cline{2-4}  & (Necrotizing Fascitis: NF) & & \\ \cline{2-4}  & Negative (Cellulitis) & & \\ \hline \multirow{3}{*}{RF: Random Forest} & Positive & \multirow{3}{*}{8} & \multirow{3}{*}{4} \\ \cline{2-4}  & (Necrotizing Fascitis: NF) & & \\ \cline{2-4}  & Negative (Cellulitis) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the test set. identifying negatives) is more critical. In this research, we focus on detecting NF patients in order to find timely treatment before symptoms become severe. Therefore, we focus on the desired model with a high recall or true positive rate percentage, which is the decision tree (DT) model. Understanding which features are most important allows us to interpret the model's decisions and improve its reliability. Figure 1 below illustrates the relative importance of the six key features used in the model, demonstrating how much each contributes to the overall prediction. Figure 1. The bar chart highlights that hemoglobin (HB) and white blood cell counts (WBC) are the most critical factors in the model's predictions, platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also play important roles, while sodium levels (NA) have the least influence among the features shown. This suggests that these features are crucial for the model to accurately identify or predict the medical condition in question, with HB and WBC being particularly significant. Platelet count is a key variable for distinguishing NF patients from those with cellulitis, ranking as the third most important feature. Furthermore, the findings of [PERSON] et al. (2020) indicate that platelet count is a valuable and cost-effective prognostic indicator for NF patients.