title: Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.
abstract: BACKGROUND
  Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke.
  
  
  METHODS
  We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data.
  
  
  RESULTS
  We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408).
  
  
  CONCLUSIONS
  We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.
authors: Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia.
authors: Hamed
authors: H
authors: Asadi
authors: Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia.
authors: Richard
authors: R
authors: Dowling
authors: Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia.
authors: Bernard
authors: B
authors: Yan
authors: Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia.
authors: Peter
authors: P
authors: Mitchell
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D002545:Brain Ischemia / Q000209:etiology* / Q000628:therapy*; D057510:Endovascular Procedures / Q000009:adverse effects*; D005260:Female; D006801:Humans; D020300:Intracranial Hemorrhages / Q000473:pathology; D008297:Male; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012372:ROC Curve; D020521:Stroke / Q000209:etiology* / Q000628:therapy*; D060388:Support Vector Machine; D016896:Treatment Outcome