Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients

Sep 17, 2026

Post-stroke depression and anxiety, collectively defined as post-stroke adverse mental outcomes (PSAMO), affect a substantial portion of stroke survivors and are linked to worse recovery, including higher mortality and greater functional disability. This study develops a machine learning model to predict PSAMO using retrospective data from 1780 patients. Participants were classified using validated depression and anxiety questionnaires, with demographic, quality-of-life, stroke, medical history, medication, and comorbidities as features. Recursive feature elimination selects inputs for eight ML algorithms, while Bayesian optimization tunes hyperparameters. Shapley additive explanations (SHAP) provide interpretability, and a gradient-boosted tree achieves 74.7% classification accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Background

Literature Review

Methods

Feature selection and model training

Hyperparameter tuning and explainability

sensors Communication Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients Chien Wei Oei 1,2, Eddie Yin Kwee Ng 2, *, Matthew Hok Shan Ng 3, Ru-San Tan 4,5, Yam Meng Chan 6, Lai Gwen Chan 7,8 and Udyavara Rajendra Acharya 9 Citation: Oei, C.W.; Ng, E.Y.K.; Ng, M.H.S.; Tan, R.-S.; Chan, Y.M.; Chan, L.G.; Acharya, U.R. Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients. Sensors 2023, 23, 7946. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s23187946 Academic Editor: Stefanos Kollias Received: 15 August 2023 Revised: 13 September 2023 Accepted: 15 September 2023 Published: 17 September 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Management Information Department, Ofﬁce of Clinical Epidemiology, Analytics and kNowledge (OCEAN), Tan Tock Seng Hospital, Singapore 308433, Singapore; [chienwei001@e.ntu.edu.sg](chienwei001@e.ntu.edu.sg) 2 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore 3 Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore 308232, Singapore; [matthewnghs@gmail.com](matthewnghs@gmail.com) 4 National Heart Centre Singapore, Singapore 169609, Singapore; [tanrsnhc@gmail.com](tanrsnhc@gmail.com) [5](5 Duke-NUS Medical School)[ Duke-NUS Medical School](5 Duke-NUS Medical School), [Singapore 169857](Singapore 169857), [Singapore](Singapore) 6 Department of General Surgery, Vascular Surgery Service, Tan Tock Seng Hospital, Singapore 308433, Singapore; [yam_meng_chan@ttsh.com.sg](yam_meng_chan@ttsh.com.sg) 7 Department of Psychiatry, Tan Tock Seng Hospital, Singapore 308433, Singapore; [lai_gwen_chan@ttsh.com.sg](lai_gwen_chan@ttsh.com.sg) 8 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore 9 School of Mathematics, Physics and Computing, University of Southern Queensland, Springﬁeld, QLD 4305, Australia; [rajendra.acharya@usq.edu.au](rajendra.acharya@usq.edu.au) * [Correspondence: mykng@ntu.edu.sg](Correspondence: mykng@ntu.edu.sg) 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 int

FAQs

What problem does the study address?

The study targets post-stroke depression and anxiety, collectively termed PSAMO, which are common after stroke and associated with poorer health outcomes.

How is the PSAMO risk prediction model built?

The model is trained on retrospective data from 1780 patients, using selected features (demographics, quality of life, stroke information, medical and medication history, comorbidities) and eight machine learning algorithms.

How do the authors interpret which factors drive the predictions?

The study applies SHAP (Shapley additive explanations) to compute feature importance and rank the features that contribute to the model’s risk prediction.

Sep 17, 2026

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