[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121858-en":3,"doc-seo-121858-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121858,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Importance analysis of psychosocial variables in frailty syndrome in heart failure patients using machine learning approach","Frailty syndrome in heart failure patients requires decision-support systems that help clinicians focus on factors driving adherence and self-care. Using supervised machine learning and permutation-based exploratory methods, the study quantifies absolute and relative diagnostic importance of Tilburg Frailty Indicator (TFI) components, integrating physical and psychosocial domains. Decision tree, random forest, and AdaBoost models identify psychological TFI20 (low mood) and TFI21 (agitation/irritability) as more diagnostically important than multiple physical variables. For remaining psychological items and all social variables, results do not reject the null hypothesis. Machine learning may highlight nonphysical origins of heart failure for multidisciplinary care.","Tilburg University  \nImportance analysis of psychosocial variables in frailty syndrome in heart failure patients using machine learning approach.  \nPasieczna, A. H. ; Szczepanowski, R,; Sobecki, J. ; Katarzyniak, R. ; Uchmanowicz, I. ;  \nGobbens, R.J.J. ; Kahsin, A. ; Dixit, A.  \nPublished in: Scientific Reports  \nDOI:  \n10.1038/s41598-023-35037-3  \nPublication date:  \n2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Tilburg University Research Portal  \nCitation for published version (APA):  \nPasieczna, A. H. , Szczepanowski, R. , Sobecki, J. , Katarzyniak, R. , Uchmanowicz, I. , Gobbens, R. J. J. , Kahsin, A. , & Dixit, A. (2023) . Importance analysis of psychosocial variables in frailty syndrome in heart failure patients using machine learning approach. Scientific Reports, 13, Article 7782. [https://doi.org/10.1038/s41598-023-](https://doi.org/10.1038/s41598-023-)[ ](https://doi.org/10.1038/s41598-023-)35037-3  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 22. Jun. 2024  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nImportance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach  \nAleksandra Helena Pasieczna1, Remigiusz Szczepanowski2*, Janusz Sobecki  \n2, Radosław Katarzyniak2, Izabella Uchmanowicz3, Robbert J. J. Gobbens4,5,6,7, Aleksander Kahsin8 & Anant Dixit2  \nThe prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on theTFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important  \nthan the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professiona","cbCailo8alYKiOBz","https://ap.wps.com/l/cbCailo8alYKiOBz","pdf",7090716,1,13,"English","en",105,"# Importance analysis of psychosocial variables in frailty syndrome in heart failure patients using machine learning approach\n## Study aim and design\n## Machine learning methods and variable importance estimation\n## Key findings: diagnostic importance of TFI components\n## Interpretation and long-term implications for clinical support","[{\"question\":\"What was the goal of the study for heart failure patients?\",\"answer\":\"To determine absolute and relative diagnostic importance of individual components of the Tilburg Frailty Indicator (TFI) in patients with heart failure using machine learning.\"},{\"question\":\"Which machine learning algorithms were used to analyze TFI data?\",\"answer\":\"The study built models using a decision tree, a random decision forest, and an AdaBoost classifier, then used permutation-based methods to estimate variable importance.\"},{\"question\":\"Which psychosocial TFI variables were most diagnostically important?\",\"answer\":\"TFI20 (low mood) and TFI21 (agitation and irritability) showed higher diagnostic importance than several physical-domain variables.\"}]","Importance analysis of psychosocial variables in frailty syndrome in heart failure patients using machine learning approach | PDF",1785807280,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"importance-analysis-of-psychosocial-variables-in-frailty-syndrome-in-heart-failure-patients-using-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/importance-analysis-of-psychosocial-variables-in-frailty-syndrome-in-heart-failure-patients-using-machine-learning-approach/121858/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the goal of the study for heart failure patients?","Question",{"text":75,"@type":76},"To determine absolute and relative diagnostic importance of individual components of the Tilburg Frailty Indicator (TFI) in patients with heart failure using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were used to analyze TFI data?",{"text":80,"@type":76},"The study built models using a decision tree, a random decision forest, and an AdaBoost classifier, then used permutation-based methods to estimate variable importance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which psychosocial TFI variables were most diagnostically important?",{"text":84,"@type":76},"TFI20 (low mood) and TFI21 (agitation and irritability) showed higher diagnostic importance than several physical-domain variables.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]