Classification of Necrotizing Fasciitis from Cellulitis Patients using Machine Learning in Maha Sarakham Hospital, Thailand

Necrotizing fasciitis (NF) is a rare but life-threatening soft tissue infection. Early diagnosis and treatment are crucial to prevent complications such as amputations and even death. However, distinguishing NF from cellulitis, a less severe skin infection, can be difficult. Machine learning (ML) has the potential to improve this distinction. Three algorithms for classification are logistic regression, decision tree, and random forests. The dataset was collected from Maha Sarakham Hospital, Thailand. It includes six features: creatinine, hemoglobin, platelet, sodium, white blood cell, and C-reactive protein. The target results consist of two classes (NF and non-NF). A trained model will be tested on a separate dataset to evaluate its performance. The C-reactive protein data has some missing values. The experiment has two cases: using all instances with missing data and removing records with missing data. Metrics such as accuracy, precision, sensitivity, specificity, and F-measure will be used. The study indicates that random forests perform well in all instances with missing data, while the decision tree is suitable for small datasets.