AUTHOR=Ou Chubin , Liu Jiahui , Qian Yi , Chong Winston , Liu Dangqi , He Xuying , Zhang Xin , Duan Chuan-Zhi 

TITLE=Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.735142

DOI=10.3389/fneur.2021.735142

ISSN=1664-2295

ABSTRACT=Background:
The prediction of aneurysm treatment outcome can help to optimize treatment strategies. Machine learning has shown positive results in many clinical areas. However, the development of such models requires expertise in machine learning, which is not an easy task for surgeons. 

Objectives:
The recently emerged automated machine learning (AutoML) has shown promise in making machine learning more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop machine learning models for treatment outcome prediction.

Methods:
Patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Statistical prediction model was developed using multivariate logistic regression. Two machine learning (ML) models were also developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC). 

Results:
Aneurysm size, stent-assisted coiling and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models.

Conclusions
This study demonstrated the feasibility of using AutoML to develop high quality ML model, which may outperform statistical model and manually derived ML models. AutoML could be a useful tool that makes machine learning more accessible to clinical researchers.