[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123300-en":3,"doc-seo-123300-105":30,"detail-sidebar-cat-0-en-105":96},{"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":20,"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},123300,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study","Chronic heart failure (CHF) poses major morbidity and mortality burdens on individuals and health systems, motivating more precise risk identification. This study uses machine learning to build a predictive model for the occurrence of CHF and evaluates CHF risk through a health ecology framework that integrates environmental, social, and individual factors. Data are obtained from the Jackson Heart Study, with preprocessing for missing values and standardization; principal component analysis and random forest guide feature selection. Multiple models are trained, balanced with SMOTE and ENN, optimized via hyperparameters, and assessed with metrics including AUC, accuracy, precision, sensitivity, and F1 under 10-fold internal cross-validation.","Machine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure:  \nHealth Ecologic Study  \nAuthor  \nXu , Qian , Cai , Xue , Yu , Ruicong , Zheng , Yueyue , Chen , Guanjie , Sun , Hui , Gao , Tianyun , Xu , Cuirong , Sun , Jing  \nPublished 2024  \nJournal Title  \nJMIR Medical Informatics  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.2196/64972  \nRights statement  \n©Qian Xu , Xue Cai , Ruicong Yu , Yueyue Zheng , Guanjie Chen , Hui Sun , Tianyun Gao , Cuirong Xu, Jing Sun. Originally published in JMIR Medical Informatics ([https://medinform.jmir.org](https://medinform.jmir.org)), 31.01.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted use , distribution , and reproduction in any medium , provided the original work , first published in JMIR Medical Informatics , is properly cited. The complete bibliographic information , a link to the original publication on [https://medinform.jmir.org/](https://medinform.jmir.org/), as well as this copyright and license information must be included.  \nDownloaded from  \n[https://hdl.handle.net/10072/436392](https://hdl.handle.net/10072/436392)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nJMIR MEDICAL INFORMATICS Xu et al  \nOriginal Paper  \nMachine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study  \n\n| Qian Xu1, BSN; Xue Cai2, PhD; Ruicong Yu 1, BSN; Yueyue Zheng3, BSN; Guanjie Chen4, MSN; Hui Sun 1, BSN; Tianyun Gao 1, BSN; Cuirong Xu5, MSN; Jing Sun6, Prof Dr |\n| --- |\n| 1School of Medicine, Southeast University, Nanjing, China\u003Cbr>2Department of Respiratory and Critical Care, Zhongda Hospital Southeast University, Nanjing, China 3Department of Geriatrics, Zhongda Hospital Southeast University, Nanjing, China\u003Cbr>4Department of Intensive Care, Zhongda Hospital Southeast University, Nanjing, China 5Department of Nursing, Zhongda Hospital Southeast University, Nanjing, China 6Rural Health Research Institute, Charles Sturt University, Orange, Australia\u003Cbr>Corresponding Author:\u003Cbr>Cuirong Xu, MSN Department of Nursing\u003Cbr>Zhongda Hospital Southeast University\u003Cbr>No.87 Dingjiaqiao Nanjing, 210009 China\u003Cbr>Phone: 86 13912990156\u003Cbr>Fax: 86 02583272077\u003Cbr>[Email: ](Email: xucuirong67@126.com)[xucuirong67@126.com](Email: xucuirong67@126.com)\u003Cbr>Abstract |\n\nBackground: Chronic heart failure (CHF) is a serious threat to human health, with high morbidity and mortality rates, imposing a heavy burden on the health care system and society. With the abundance of medical data and the rapid development ofmachine learning (ML) technologies, new opportunities are provided for in-depth investigation of the mechanisms of CHF and the construction of predictive models. The introduction of health ecology research methodology enables a comprehensive dissection ofCHF risk factors from a wider range of environmental, social, and individual factors. This not only helps to identify high-risk groups at an early stage but also provides a scientific basis for the development of precise prevention and intervention strategies. Objective: This study aims to use ML to construct a predictive model of the risk of occurrence of CHF and analyze the risk of CHF from a health ecology perspective.  \nMethods: This study sourced data from the Jackson Heart Study database. Stringent data preprocessing procedures were implemented, which included meticulous management of missing values and the standardization of data. Principal component analysis and random forest (RF) were used as feature selection techniques. Subsequently, several ML models, namely decision tree, RF, extreme gradient boosting, adaptive boosting (AdaBoost), support vector machine, n","cbCaib9dhaoafT6t","https://ap.wps.com/l/cbCaib9dhaoafT6t","pdf",904468,1,19,"English","en",105,"# Abstract\n## Background and Objective\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the study objective regarding chronic heart failure?\",\"answer\":\"To construct a machine learning–based predictive model for the occurrence of chronic heart failure and analyze risk from a health ecology perspective.\"},{\"question\":\"Which dataset is used to develop and validate the models?\",\"answer\":\"The study uses data sourced from the Jackson Heart Study database and performs internal validation using 10-fold cross-validation.\"},{\"question\":\"How are features selected and how is class imbalance handled?\",\"answer\":\"Feature selection uses principal component analysis and random forest; data balancing is improved using SMOTE and Edited Nearest Neighbors.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"AdaBoost was most effective, with the reported AUC and classification metrics, and model performance was compared using accuracy, precision, sensitivity, F1-score, and AUC.\"}]","Machine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study | PDF",1785815817,48,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"machine-learningbased-risk-factor-analysis-and-prediction-model-construction-for-the-occurrence-of-chronic-heart-failure-health-ecologic-study","",{"@graph":36,"@context":90},[37,54,69],{"@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/machine-learningbased-risk-factor-analysis-and-prediction-model-construction-for-the-occurrence-of-chronic-heart-failure-health-ecologic-study/123300/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study objective regarding chronic heart failure?","Question",{"text":76,"@type":77},"To construct a machine learning–based predictive model for the occurrence of chronic heart failure and analyze risk from a health ecology perspective.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which dataset is used to develop and validate the models?",{"text":81,"@type":77},"The study uses data sourced from the Jackson Heart Study database and performs internal validation using 10-fold cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"How are features selected and how is class imbalance handled?",{"text":85,"@type":77},"Feature selection uses principal component analysis and random forest; 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