Development of Predictive Models with Random Forest and XGBoost for Risk Stratification in Stroke Patients - Tranquilino, Douglas; Vinicius, Marcos; de Carli, Rafael; Callou, Gustavo; Tavares, Eduardo; Bezerra, Thiago (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2025)
Machine learning (ML) is transforming clinical medicine by enabling the analysis of complex datasets to identify predictive patterns. This capability makes ML particularly valuable in neurology for prognosticating outcomes in complex conditions like stroke. This paper presents a prediction system for clinical risk stratification in stroke patients using ML and open-source data. The system utilizes a comprehensive panel of clinical and laboratory data, including hematological, metabolic, and inflammatory markers, to predict the patient’s condition. Two ensemble algorithms, Random Forest and XGBoost, were developed and compared. The results demonstrate the feasibility of this approach in enhancing prognostic accuracy.