Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients
Article
Oei, Chien Wei, Ng, Eddie Yin Kwee, Shan, Matthew Hok, Tan, Ru-San, Chan, Yam Meng, Chan, Lai Gwen and Acharya, Udyavara Rajendra. 2023. "Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients." Sensors. 23 (18). https://doi.org/10.3390/s23187946
Article Title | Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Oei, Chien Wei, Ng, Eddie Yin Kwee, Shan, Matthew Hok, Tan, Ru-San, Chan, Yam Meng, Chan, Lai Gwen and Acharya, Udyavara Rajendra |
Journal Title | Sensors |
Journal Citation | 23 (18) |
Article Number | 7946 |
Number of Pages | 12 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s23187946 |
Web Address (URL) | https://www.mdpi.com/1424-8220/23/18/7946 |
Abstract | Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO. |
Keywords | artificial intelligence; automated risk prediction; post-stroke adverse mental outcome; machine learning |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Tan Tock Seng Hospital, Singapore |
Nanyang Technological University, Singapore | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
School of Mathematics, Physics and Computing |
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