Progression Prediction with Machine Learning Methods among the Anterior Choroidal Artery Infarction

Background Patients with anterior choroidal artery (AChA) infarction often experience fluctuating symptoms or neurological progression despite aggressive treatment. Machine learning, known for its high accuracy, is increasingly applied in medicine. This study explored the use of machine learning techniques to predict neurological progression in AChA infarction patients. Methods A total of 369 AChA infarction patients were retrospectively enrolled. Demographic and clinical characteristics were collected and analyzed. Clinical features were compared between patients with and without neurological progression. Six machine learning models—logistic regression (LR), random forest (RF), decision tree (DT), eXtreme Gradient Boosting (XGB), support vector machine (SVM), and K-Nearest Neighbour (KNN)—were employed to predict progression. Their performance was evaluated using the area under the receiver operating characteristic curve (AUC ROC), accuracy (ACC), and F1 score (F1). Results Neurological progression occurred in one-third of AChA infarction patients. Hemiparesis was the most common manifestation (87.8%), occurring more frequently in the progression group than in the no-progression group (95.1% vs. 84.1%, P = 0.002). Although more men were affected (75.3%), they had a lower likelihood of progression (66.7% vs. 79.7%, P = 0.006). No significant differences were observed in stroke location or intravenous rt-PA therapy between the groups. Among the models, RF demonstrated the best predictive performance, achieving the highest AUC ROC (0.851), accuracy (70.3%), and F1 score (52.2%). The top three predictors identified by RF were NIHSS score at peak, NIHSS score at admission, and age. Conclusion Motor deficits are the most frequent and characteristic symptoms in AChA infarcts. RF emerged as the most effective model for predicting clinical progression in these patients, offering a simple and useful tool for risk assessment. Special attention should be given to NIHSS scores at peak and admission, as well as patient age.