Fusion Logistic Regression: Advancing Towards Stroke Early Warning

Abstract

 In this study, we focus on developing a logistic regression-based binary classification model to identify potential hemorrhagic stroke patients accurately. The model utilizes personal history, medical records, onset and treatment information from a training set of 100 hemorrhagic stroke patients. The objective is to predict the extent of hematoma expansion, assessing whether patients face significant health risks. Compared to traditional methods for identifying hemorrhagic strokes, such as decision trees, support vector machines, random forests, and gradient boosting machines, our logistic regression model demonstrates significant advantages in performance metrics such as F1 score and recall. Moreover, it achieves an accuracy rate of 96% in testing, surpassing other comparative models. Additionally, the model provides each patient with precise disease probability predictions, aiding in early treatment, alleviating economic burdens, and substantially reducing the technical complexity for medical professionals during the diagnosis and treatment process.