Modified Laboratory Risk Indicator and Machine Learning in Classifying Necrotizing Fasciitis from Cellulitis Patients

Abstract

Necrotizing fasciitis (NF) is a severe and life-threatening soft tissue infection that requires timely and accurate diagnosis to improve patient outcomes. The early diagnosis of NF remains challenging due to its similarity to other subcutaneous soft tissue infections like cellulitis. This study aims to employ machine learning techniques to differentiate NF from cellulitis and enhance the diagnostic accuracy of NF by developing a modified LRINEC (MLRINEC) score. These modifications aimed to improve the sensitivity and specificity of NF diagnosis. The study utilized three machine learning classifiers—Logistic Regression, decision tree, and Random Forest—to assess their effectiveness in distinguishing between NF and cellulitis cases. The MLRINEC score was developed by incorporating six key blood test parameters: creatinine, hemoglobin, platelet count, sodium, white blood cell count, and C-reactive protein using laboratory data from Maha Sarakham Hospital in Northeastern Thailand. Our findings indicate that the decision tree classifier demonstrated superior performance, achieving the highest recall, particularly in accurately identifying NF cases. A feature importance analysis revealed that hemoglobin levels and white blood cell counts were the most critical factors influencing the model’s predictions. The platelet count (PT), C-reactive protein (CRP), and creatinine (CT) also played important roles, while sodium levels (NA) were the least influential. The MLRINEC score demonstrates high accuracy in classifying NF and cellulitis patients, paving the way for improved diagnostic protocols in clinical settings.