[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118355-en":3,"doc-seo-118355-105":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},118355,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women - Research Findings","Perimenopause involves ovarian function decline and estrogen reduction, triggering multi-system physiological changes that increase cardiovascular disease risk; major adverse cardiovascular events (MACE) include heart failure and myocardial infarction. A cohort of 411 perimenopausal women was split into training and test sets to construct MACE risk prediction models using random forest, backpropagation neural network, and logistic regression, then evaluate performance by accuracy, sensitivity, specificity, and AUC. Results show the random forest model achieved the highest ROC AUC (0.948), supporting early identification of high-risk patients.","International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInternational Journal of General Medicine  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nConstruction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women  \nAnjing Chen 1 , *, Xinyue Chang2 , *, Xueling Bian 1 , Fangxia Zhang 3 , Shasha Ma4 , Xiaolin Chen 5  \n1College of Nursing, Binzhou Medical University, Shandong, 256600, People’s Republic of China; 2Vascular Surgery Department, Shandong Provincial Hospital, Binzhou, Shandong, 250001, People’s Republic of China; 3CCU, Binzhou Medical University Hospital, Binzhou, Shandong, 256600, People’s Republic of China; 4Neurology Department, Binzhou Medical University Hospital, Binzhou, Shandong, 256600, People’s Republic of China; 5Office of Health Care, Binzhou Medical University Hospital, Binzhou, Shandong, 256600, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Xiaolin Chen, Office of Health Care, Binzhou Medical University Hospital, No. 661, Huanghe 2nd Road, Bincheng District, Binzhou, Shandong, 256600, People’s Republic of China, Tel +86 13854380372, Email [xiaolin750210@sina.com](xiaolin750210@sina.com)  \n\n| Background: Perimenopausal period is a period of physiological changes in women with signs of ovarian failure, including menopausal transition period and 1 year after menopause. Ovarian function declines in perimenopausal women and lower estrogen levels lead to changes in the function of various organs, which may lead to cardiovascular disease. Major adverse cardiovascular events (MACE) are the combination of clinical events including heart failure, myocardial infarction and other cardiovascular diseases. Therefore, this study explores the factors influencing the occurrence of MACE in perimenopausal women and establishes a prediction model for MACE risk factors using three algorithms, comparing their predictive performance.\u003Cbr>Patients and Methods: A total of 411 perimenopausal women diagnosed with MACE at the Binzhou Medical University Hospital were randomly divided into a training set and a test set following a 7:3 ratio. According to the principle of 10 events per Variable, the training set sample size was sufficient. In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. The prediction performance of the model was evaluated in terms of accuracy, sensitivity, specificity, and area under the subject operating characteristic curve (AUC) .\u003Cbr>Results: A total of twenty-six candidate variables were included. The area under ROC curve of the RF model, BPNN model, and logistic regression model was 0.948, 0.921, and 0.866. Comparison of ROC curve AUC between logistic regression and RF model for predicting MACE risk showed a statistically significant difference (Z=2.278, P=0.023) .\u003Cbr>Conclusion: The RF model showed good performance in predicting the risk of MACE in perimenopausal women providing a reference for the early identification of high-risk patients and the development of targeted intervention strategies.\u003Cbr>Keywords: major adverse cardiovascular events, machine learning, perimenopause, risk factor |\n| --- |\n| Introduction\u003Cbr>Perimenopause is a stage of normal physiological changes in women characterized by the depletion of viable follicles in the ovaries leading to the permanent cessation of menstruation. Currently, over 850 million women worldwide are in perimenopause, and the total perimenopausal population in China will account for approximately 14.29% of the world’s population by 2030.1,2 Perimenopause triggers multi-system physiological changes causing a decline in estrogen levels, but also increased body fat and un","cbCaijWSp6nV07hj","https://ap.wps.com/l/cbCaijWSp6nV07hj","pdf",1805290,1,10,"English","en",105,"# Background\n# Patients and Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the study aiming to do in perimenopausal women?\",\"answer\":\"The study explores factors related to MACE occurrence in perimenopausal women and builds prediction models for MACE risk using three machine-learning algorithms, then compares their predictive performance.\"},{\"question\":\"How were the prediction models constructed and evaluated?\",\"answer\":\"A total of 411 perimenopausal women were randomly divided into training and test sets in a 7:3 ratio. Model performance was assessed using accuracy, sensitivity, specificity, and AUC based on ROC curves.\"},{\"question\":\"Which algorithm performed best for predicting MACE risk?\",\"answer\":\"The random forest model showed the best performance with an ROC AUC of 0.948, outperforming the backpropagation neural network (0.921) and logistic regression (0.866).\"},{\"question\":\"What is the practical significance of the findings?\",\"answer\":\"A well-performing risk model can help clinicians identify high-risk perimenopausal patients early and support development of targeted intervention strategies to reduce MACE incidence and improve quality of life.\"}]","Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women - Research Findings | PDF",1785683258,25,{"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},"construction-and-comparison-of-machine-learning-based-risk-prediction-models-for-major-adverse-cardiovascular-events-in-perimenopausal-women-research-findings","",{"@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/construction-and-comparison-of-machine-learning-based-risk-prediction-models-for-major-adverse-cardiovascular-events-in-perimenopausal-women-research-findings/118355/",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","2026-08-02",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 aiming to do in perimenopausal women?","Question",{"text":76,"@type":77},"The study explores factors related to MACE occurrence in perimenopausal women and builds prediction models for MACE risk using three machine-learning algorithms, then compares their predictive performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the prediction models constructed and evaluated?",{"text":81,"@type":77},"A total of 411 perimenopausal women were randomly divided into training and test sets in a 7:3 ratio. Model performance was assessed using accuracy, sensitivity, specificity, and AUC based on ROC curves.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithm performed best for predicting MACE risk?",{"text":85,"@type":77},"The random forest model showed the best performance with an ROC AUC of 0.948, outperforming the backpropagation neural network (0.921) and logistic regression (0.866).",{"name":87,"@type":74,"acceptedAnswer":88},"What is the practical significance of the findings?",{"text":89,"@type":77},"A well-performing risk model can help clinicians identify high-risk perimenopausal patients early and support development of targeted intervention strategies to reduce MACE incidence and improve quality of life.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":21,"slug":138},"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]