Predicting shit like this is useless, Survivors want recovery! DELIVER THAT!
Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability
About the Authors
- Qian Ye
Roles Data curation, Writing – original draft
‡ QY, GF also contributed equally to this work and share first authorship.
Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Guilin Fang
Roles Data curation, Formal analysis
‡ QY, GF also contributed equally to this work and share first authorship.
Affiliation Department of Nephrology, Nanjing Jinling Hospital, General Hospital of Eastern Theatre Command, Nanjing, Jiangsu, China
- Liping Li
Roles Data curation
Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Qinggui Li
Roles Investigation
Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Yun Yang
Roles Investigation
Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Lingling Liu
Roles Formal analysis, Methodology, Writing – review & editing
* E-mail: 1209674467@qq.com
Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
https://orcid.org/0009-0006-7823-5899Competing Interests
The authors have declared that no competing interests exist.
Abstract
Purpose
We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key predictors to guide rehabilitation decisions.
Materials and methods
Data of 589 stroke inpatients (2019–2024) were split into good (BI ≥ 60) and poor (BI < 60) ADL groups. Continuous variables were processed using Z-score normalization, followed by preliminary univariate regression screening (P < 0.05) and final feature selection via LASSO regression (lambda.1se = 0.0488). The screened features were used to train and validate ten machine learning algorithms; 30% of the dataset (n = 177) was allocated as an independent test set for model evaluation, and SHAP analysis was performed to interpret the optimal model.
Results
Six of 41 features were retained. Random forest achieved the best performance (AUC = 0.958; accuracy = 0.936; sensitivity = 0.934; specificity = 0.950). SHAP identified the top drivers: admission Barthel Index, standing balance, Brunnstrom stages (upper and lower limb), dressing, and grooming abilities.
Figures
Citation: Ye Q, Fang G, Li L, Li Q, Yang Y, Liu L (2026) Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability. PLoS One 21(7): e0351468. https://doi.org/10.1371/journal.pone.0351468
Editor: Fan Zhang, Longhua Hospital Shanghai University of Traditional Chinese Medicine, CHINA
Received: November 28, 2025; Accepted: May 26, 2026; Published: July 2, 2026
Copyright: © 2026 Ye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data for this study are publicly available from the Zenodo repository (https://doi.org/10.5281/zenodo.20629742).
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
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