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Machine learning-based feature selection can identify parameters with higher performance in outcome prediction.

Abstract

BACKGROUND AND PURPOSE

Figure from Jiang et al — Receiver operating characteristics (ROCs) of XGB prediction models with clinical features, imaging features, both clinical and imaging features, best-performing features, and SPAN-100 for predicting a 90-day mRS score of >2. For all patients and recanalized and nonrecanalized patients, the AUCs of models with the best-performing features were higher than those in SPAN-100, and statistical significance was reached in the total and nonrecanalized groups. The AUCs for machine learning models with the 6 best-performing features in the total cohort and recanalized and nonrecanalized groups were 0.80, 0.79, and 0.82, respectively. The AUCs for SPAN-100 were 0.78, 0.76, and 0.78, respectively. The AUCs of XGB models with the best-performing features were higher than those in SPAN-100 and reached statistical significance for the total cohort (P < .05) and the nonrecanalized patients (P < .001). In the recanalized group, the difference was not significant (P = .05).

Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model.

MATERIALS AND METHODS

A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90 days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis.

RESULTS

In 3 groups of patients, the baseline NIHSS was the most significant predictor of outcome among all the parameters, with relative weights of 0.36∼0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29∼0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical significance for the total (P < .05) and the nonrecanalized patients (P < .001).

CONCLUSIONS

Machine learning–based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.

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Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

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