Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS)

Mirko Jerber Rodríguez Mallma
* ORCID logo ,
Marcos Vilca-Aguilar
,
Luis Zuloaga-Rotta
ORCID logo ,
Rubén Borja-Rosales
,
María Salas-Ojeda
,
David Mauricio
Version 1 : Received: 4 July 2023 / Approved: 5 July 2023 / Online: 5 July 2023 (04:35:09 CEST)

A peer-reviewed article of this Preprint also exists.

Rodríguez Mallma, M.J.; Vilca-Aguilar, M.; Zuloaga-Rotta, L.; Borja-Rosales, R.; Salas-Ojeda, M.; Mauricio, D. Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS). Diagnostics 2024, 14, 22. https://doi.org/10.3390/diagnostics14010022 Rodríguez Mallma, M.J.; Vilca-Aguilar, M.; Zuloaga-Rotta, L.; Borja-Rosales, R.; Salas-Ojeda, M.; Mauricio, D. Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS). Diagnostics 2024, 14, 22. https://doi.org/10.3390/diagnostics14010022

Abstract

In this study, we developed a model that can predict whether patients with cerebral arteriovenous malformation (AVM) will be cured 36 months after intervention by means of stereotactic radiosurgery (SRS), and identified the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 92.16%, sensitivity of 92.86%, specificity of 88.89%, precision of 97.50%, and AUC of 97.62%, which shows that ML models are suitable for use for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AMV would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

Keywords

brain arteriovenous malformation; prognosis; prediction; machine learning; artificial intelligence; decision tree; logistic regression

Subject

Medicine and Pharmacology, Neuroscience and Neurology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.