## 3 Results
### Machine Learning in Classifying Necotizing Fascitis from Cellulitis Patients  
The dataset of this study has six independent variables: serum creatinine (CT), hemoglobin (HB), platelet count (PT), serum sodium (NA), white blood cell count (WBC), and C-reactive protein (CRP). All the variables are numeric values, while the CRP is in discrete value (positive and negative) and numeric form. If a numeric value is higher than 150 mg/L, it is defined as \"Positive\"; otherwise, it is \"Negative\". The target values (dependent variable) of the study are divided into two categories: NF and non-NF. Table 2 presents the statistical data on various blood test measurements, comparing individuals with a condition labeled \"NF\" (potentially a specific condition group) to those without it. Key findings include the following:  
Creatinine: NF individuals generally have slightly lower coagulation test (CT) values than those without NF. Hemoglobin Levels: Hemoglobin levels are lower on average in NF individuals compared to non-NF individuals. Platelet Counts: Platelet counts are higher in NF individuals. Sodium Levels: Sodium levels are slightly lower in NF individuals. White Blood Cell Counts: NF individuals have higher white blood cell (WBC) counts, which may indicate an inflammatory or immune response. C-Reactive Protein (CRP): The CRP data are presented as the counts of individuals with either negative or positive results, rather than as continuous measurements. Table 3 presents the confusion matrix data for the three classification models--Logistic Regression (LR), decision tree classifier (DT), and Random Forest (RF)--assessed on a test set with two classes: positive (necrotizing fascitis) and negative (cellulitis). Logistic Regression (LR) shows a balanced number of true positives and true negatives but also exhibits a relatively high rate of false positives and false negatives. Decision tree classifier (DT) excels with the highest number of true positives and no false positives, though it does have some false negatives, indicating it is highly effective at identifying necrotizing fascitis while minimizing false positives. Random Forest (RF) demonstrates a good number of true positives but also has some false positives and false negatives, reflecting moderate overall performance. Among the models, the decision tree classifier (DT) performs the best in accurately identifying positive cases (necrotizing fascitis) without misclassifying negative cases (cellulitis) as positive. Logistic Regression (LR) and Random Forest (RF) each have trade-offs between false positives and false negatives, with Random Forest slightly better at minimizing false positives but performing worse than decision tree on false negatives. Table 4 presents the evaluation results of the three classification models--Logistic Regression (LR), decision tree (DT), and Random Forest (RF)--expressed as percentages. Decision tree (DT) shows the best performance in recall and F-measure, making it highly effective at identifying positive instances but at the cost of lower specificity. Logistic Regression (LR) provides high precision and specificity, but its recall and F-measure are moderate. Random Forest (RF) has good recall and a balanced F-measure but lower specificity, showing it is effective at identifying positives while struggling more with negatives. These results suggest that the best model depends on the specific application and whether prioritizing recall (sensitivity to detecting positives) or specificity (accuracy in