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Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

Author

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  • Hamed Asadi
  • Richard Dowling
  • Bernard Yan
  • Peter Mitchell

Abstract

Introduction: 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. Method: 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). Discussion: 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.

Suggested Citation

  • Hamed Asadi & Richard Dowling & Bernard Yan & Peter Mitchell, 2014. "Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
  • Handle: RePEc:plo:pone00:0088225
    DOI: 10.1371/journal.pone.0088225
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    Cited by:

    1. Yao Tong & Beilei Lin & Gang Chen & Zhenxiang Zhang, 2022. "Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study," IJERPH, MDPI, vol. 19(3), pages 1-18, January.
    2. Esra Zihni & Vince Istvan Madai & Michelle Livne & Ivana Galinovic & Ahmed A Khalil & Jochen B Fiebach & Dietmar Frey, 2020. "Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
    3. Wieslaw L Nowinski & Varsha Gupta & Guoyu Qian & Wojciech Ambrosius & Radoslaw Kazmierski, 2014. "Population-Based Stroke Atlas for Outcome Prediction: Method and Preliminary Results for Ischemic Stroke from CT," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.
    4. Wenjuan Wang & Martin Kiik & Niels Peek & Vasa Curcin & Iain J Marshall & Anthony G Rudd & Yanzhong Wang & Abdel Douiri & Charles D Wolfe & Benjamin Bray, 2020. "A systematic review of machine learning models for predicting outcomes of stroke with structured data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    5. Yizhao Ni & Kathleen Alwell & Charles J Moomaw & Daniel Woo & Opeolu Adeoye & Matthew L Flaherty & Simona Ferioli & Jason Mackey & Felipe De Los Rios La Rosa & Sharyl Martini & Pooja Khatri & Dawn Kle, 2018. "Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-20, February.

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