Research Paper|Volume 16, Issue 5|pp 4654—4669

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

Lei Shu^1,^2,^3,⁴, Hua Yan⁵, Yanze Wu^1,^2,^3,⁴, Tengfeng Yan^1,^2,^3,⁴, Li Yang^1,^2,^3,⁴, Si Zhang^1,^2,^3,⁴, Zhihao Chen^1,^2,^3,⁴, Qiuye Liao^1,^2,^3,⁴, Lu Yang^1,^2,^3,⁴, Bing Xiao¹, Minhua Ye¹, Shigang Lv¹, Miaojing Wu¹, Xingen Zhu^1,^2,^3,⁴, Ping Hu^1,^2,^3,⁴

¹Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China
²Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang 330006, Jiangxi, China
³Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang 330006, Jiangxi, China
⁴Institute of Neuroscience, Nanchang University, Nanchang 330006, Jiangxi, China
⁵Department of Emergency, Affiliated Hospital of Panzhihua University, Panzhihua 617000, Sichuan, China

* Equal contribution

Received: September 5, 2023Accepted: January 29, 2024Published: March 1, 2024

https://doi.org/10.18632/aging.205621

Copyright: © 2024 Shu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objective: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP).

Methods: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis.

Results: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions.

Conclusions: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.