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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.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/explainable-risk-prediction-of-post-stroke-adverse-mental-outcomes-using-machine-learning-techniques-in-a-population-of-1780-patients/290321/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/explainable-risk-prediction-of-post-stroke-adverse-mental-outcomes-using-machine-learning-techniques-in-a-population-of-1780-patients/290321.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address?","Question",{"text":113,"@type":114},"The study targets post-stroke depression and anxiety, collectively termed PSAMO, which are common after stroke and associated with poorer health outcomes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the PSAMO risk prediction model built?",{"text":118,"@type":114},"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.",{"name":120,"@type":111,"acceptedAnswer":121},"How do the authors interpret which factors drive the predictions?",{"text":122,"@type":114},"The study applies SHAP (Shapley additive explanations) to compute feature importance and rank the features that contribute to the model’s risk prediction.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290321,1789641340,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","sensors   \nCommunication  \nExplainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients  \nChien 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  \nCitation: 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  \nAcademic Editor: Stefanos Kollias  \nReceived: 15 August 2023  \nRevised: 13 September 2023  \nAccepted: 15 September 2023  \nPublished: 17 September 2023  \nCopyright: © 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/)) .  \n1 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)  \n2 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore  \n3 Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore 308232, Singapore; [matthewnghs@gmail.com](matthewnghs@gmail.com)  \n4 National Heart Centre Singapore, Singapore 169609, Singapore; [tanrsnhc@gmail.com](tanrsnhc@gmail.com)  \n[5](5 Duke-NUS Medical School)[ Duke-NUS Medical School](5 Duke-NUS Medical School), [Singapore 169857](Singapore 169857), [Singapore](Singapore)  \n6 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)  \n7 Department of Psychiatry, Tan Tock Seng Hospital, Singapore 308433, Singapore; [lai_gwen_chan@ttsh.com.sg](lai_gwen_chan@ttsh.com.sg)  \n8 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore  \n9 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4305, Australia; [rajendra.acharya@usq.edu.au](rajendra.acharya@usq.edu.au)  \n* [Correspondence: mykng@ntu.edu.sg](Correspondence: mykng@ntu.edu.sg)  \nAbstract: 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","cbCait4ECGYcORUM","https://ap.wps.com/l/cbCait4ECGYcORUM","pdf",1313990,12,"English","# Introduction\n## Background\n## Literature Review\n# Methods\n## Feature selection and model training\n## Hyperparameter tuning and explainability","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets post-stroke depression and anxiety, collectively termed PSAMO, which are common after stroke and associated with poorer health outcomes.\"},{\"question\":\"How is the PSAMO risk prediction model built?\",\"answer\":\"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.\"},{\"question\":\"How do the authors interpret which factors drive the predictions?\",\"answer\":\"The study applies SHAP (Shapley additive explanations) to compute feature importance and rank the features that contribute to the model’s risk prediction.\"}]","Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients | PDF"]