Abstract
OBJECTIVE: Cerebral venous thrombosis (CVT) is a rare but significant condition, primarily affecting young adults, especially women. The diagnosis of CVT is challenging due to its nonspecific clinical presentation. Inflammatory biomarkers, such as the systemic immune-inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), may aid in early diagnosis. This study aimed to explore the role of these biomarkers and assess machine learning models for improving diagnostic accuracy. METHODS: This study included 100 CVT patients and 50 controls. Data collected included demographic information, biochemical markers, and clinical symptoms. Traditional statistical methods and machine learning models, including decision trees, random forests, AdaBoost, k-nearest neighbors, support vector machines (SVM), and artificial neural networks (ANN), were used to evaluate the diagnostic value of biomarkers. RESULTS: The SII and NLR levels were significantly higher in CVT patients. The ANN model based on SII and PLR achieved the best diagnostic performance, with an area under the curve (AUC) of 0.94, showing high accuracy and reliability. CONCLUSION: Inflammatory biomarkers, particularly SII, have significant predictive value in CVT diagnosis. Machine learning models, especially ANN, show promise in improving diagnostic accuracy. Future studies with larger sample sizes are needed to validate these findings further.