Objective: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision-making process involves numerous biomarkers including imaging features and demographic information. The objective of this study was to integrate common stroke biomarkers using machine learning methods and predict patient recovery outcome at 90 days.
Materials and methods: A total of 512 patients were enrolled in this retrospective study. Extreme gradient boosting (XGB) and gradient boosting machine (GBM) models were used to predict modified Rankin scale (mRS) scores at 90 days using biomarkers available at admission and 24 hours. Feature selections were performed using a greedy algorithm. Fivefold cross validation was applied to estimate model performance.
Results: For binary prediction of an mRS score of greater than 2 using biomarkers available at admission, XGB and GBM had an AUC of 0.746 and 0.748, respectively. Adding the National Institutes of Health Stroke Score at 24 hours and performing feature selection improved the AUC of XGB to 0.884 and the AUC of GBM to 0.877. With the addition of the recanalization outcome, XGB's AUC improved to 0.807 for nonrecanalized patients and dropped to 0.670 for recanalized patients. GBM's AUC improved to 0.781 for nonrecanalized patients and dropped to 0.655 for recanalized patients.
Conclusion: Decision tree-based GBMs can predict the recovery outcome of stroke patients at admission with a high AUC. Breaking down the patient groups on the basis of recanalization and nonrecanalization can potentially help with the treatment decision process.
Keywords: CT; machine learning; modified Rankin scale; prediction; stroke.