## 5 Conclusions  
Machine learning techniques, particularly the decision tree (DT) classifier, are effective in differentiating necrotizing fascitits (NF) from other soft tissue infections like cellulitis. The DT model exhibited the highest accuracy in identifying NF cases with a recall rate of 100%, making it the most suitable model for early detection and treatment. While Logistic Regression (LR) offered high precision and specificity, it had moderate recall. Random Forest (RF) showed balanced performance but struggled with specificity. Overall, the choice of the best model depends on the application's specific needs, whether prioritizing sensitivity to detecting positive cases (recall) or accuracy in identifying negative cases (specificity). As a result of this research, we recommend using the decision tree model because it gives the highest recall or true positive value of 100 percent. The positive group is the NF patient group. If we can use the model to accurately predict this group, it will lead to receiving urgent treatment and can reduce the mortality rate for NF patients. Feature importance analysis revealed that hemoglobin (HB) and white blood cell counts (WBC) were the most significant predictors in distinguishing NF from cellulitis. The platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also played important roles, while sodium levels (NA) were the least influential.  
Moreover, the research modifies the Laboratory Risk Indicator for Necrotizing Fascitits (LRINEC) score by substituting platelet counts for glucose levels in the original LRINEC score from [3], thereby improving the tool's predictive capability. Combining traditional risk scoring methods with machine learning models offers a more accurate assessment of NF risk, potentially enhancing patient outcomes by enabling earlier and more precise intervention. The inclusion of platelet count as a key variable is especially noteworthy. The findings suggest that incorporating advanced analytical techniques and refining the existing diagnostic tools can significantly enhance clinical decision making and patient care.  
Conceptualization, S.S. and P.S.; methodology, S.S.; software, R.C.; validation, P.S., R.C. and S.S.; formal analysis, R.C.; investigation, K.K.; resources, K.K.; data curation, K.K.; writing--original draft preparation, S.S.; writing--review and editing, K.K.; visualization, S.S.; supervision, P.B.; project administration, P.B.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Thailand Science Research and Innovation (TSRI) 2023. The APC was funded by Mahasarakham university, Thailand.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Mahasarakham University (protocol code 395-374/2564 and date of approval 16 December 2021).  
Patient consent was waived due to the study used secondary data from a hospital that did not detail the patient's personal information in depth.  
**Acknowledgments:** Thank you to Maha Sarakham Hospital for providing extremely valuable information for this research. The authors are grateful to the reviewers for their valuable and constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.