A machine learning approach to select features important to stroke prognosis

Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-term disability in the world. Selection of features related to stroke prognosis is highly valuable for effective intervention and treatment. In this study, an integrated machine learning approach was used to select the features as prognosis factors of stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importance of features was ranked by the Shapiro-Wilk algorithm and the Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features. Furthermore, the importance of selected features was determined by Random-Forest-Classifier and Shapiro-Wilk algorithm. Finally, twenty-three selected features were used by SVC, MLP, Random-Forest, and AdaBoost-Classifier to predict the RVISINF (Infarct visible on CT) of acute stroke on IST dataset. It was suggested that the selected features could be used to infer the long-term prognosis of acute stroke at a high accuracy, and it also could be used to extract factors related to RVISINF, which is associated with large artery occlusion (LAO) in ischemic stroke patient.