[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123631-en":3,"doc-seo-123631-105":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},123631,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning in Hypertension Detection - A Study on World Hypertension Day Data","Many modifiable and non-modifiable risk factors are linked to hypertension, yet screening programs still miss individuals at higher risk. Because high blood pressure drives cardiovascular events and mortality, new detection strategies are needed. This study assesses whether machine learning can identify predictors of hypertension using World Hypertension Day questionnaire data from 2015 to 2019. Five algorithms with different balancing techniques are evaluated alongside the current medical screening protocol, and results show sensitivity–specificity trade-offs, with Random Forest providing comparatively informative performance.","Journal of Medical Systems (2023) 47:1  \n[https://doi.org/10.1007/s10916-022-01900-5](https://doi.org/10.1007/s10916-022-01900-5)  \nMachine Learning in Hypertension Detection: A Study on World Hypertension Day Data  \nSara Montagna1 · Martino Francesco Pengo2,3 · Stefano Ferretti1 · Claudio Borghi4 · Claudio Ferri5 · Guido Grassi3 · Maria Lorenza Muiesan6,7 · Gianfranco Parati2,3  \nReceived: 8 November 2022 / Accepted: 6 December 2022 © The Author(s) 2022  \nAbstract  \nMany modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity – 0.629 specificity) compared with medical protocols (0.906 sensitivity – 0.230 specificity) . Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models.  \nKeywords Hypertension · Data analysis · Prevention Introduction  \nArterial hypertension still remains the most important modifiable risk factor for cardiovascular disease worldwide. Despite extensive knowledge about ways to prevent and treat hypertension, the global incidence and prevalence  \nof hypertension and its cardiovascular complications are still elevated mainly due to inadequacies in prevention, detection and control [1, 2] . The high variability characterising blood pressure (BP) values, together with the lack of specific symptoms of this condition, make the detection of hypertension still challenging.  \nSara Montagna and Martino Pengo contributed equally to this work.  \n* Sara Montagna[sara.montagna@uniurb.it](sara.montagna@uniurb.it)[ ](sara.montagna@uniurb.it)Martino Francesco Pengo [martino.pengo@unimib.it](martino.pengo@unimib.it)[ ](martino.pengo@unimib.it)Stefano Ferretti  \n[stefano.ferretti@uniurb.it](stefano.ferretti@uniurb.it)  \nClaudio Borghi [claudio.borghi@unibo.it](claudio.borghi@unibo.it)[ ](claudio.borghi@unibo.it)Claudio Ferri [claudio.ferri@cc.univaq.it](claudio.ferri@cc.univaq.it)[ ](claudio.ferri@cc.univaq.it)Guido Grassi [guido.grassi@unimib.it](guido.grassi@unimib.it)  \nMaria Lorenza Muiesan  \n[marialorenza.muiesan@unibs.it](marialorenza.muiesan@unibs.it)  \nGianfranco Parati  \n[gianfranco.parati@unimib.it](gianfranco.parati@unimib.it)  \n1 DiSPeA–University of Urbino Carlo Bo, Piazza della Repubblica 13, Urbino 61029, Italy  \n2 Istituto Auxologico Italiano IRCCS, Milan, Italy  \n3 SMS–University of Milano Bicocca, Milan, Italy  \n4 University of Bologna, Bologna, Italy  \n5 MESVA–University of L’Aquila, L’Aquila, Italy  \n6 DSCS–University of Brescia, Brescia, Italy  \n7 Spedali Civili 1, Brescia, Italy  \nSince 2005, the World Hypertension League has been leading a global campaign to raise awareness of the importance of hypertension through annual screening programs. During the 2018 survey, among 502079 p","cbCaiuxGcsWNC8wB","https://ap.wps.com/l/cbCaiuxGcsWNC8wB","pdf",1502126,1,10,"English","en",105,"# Abstract\n# Introduction\n## Hypertension as a major cardiovascular risk factor\n## Limitations of current screening programs\n## Role of AI and machine learning in healthcare\n# Study Design and Data\n## World Hypertension Day dataset (2015–2019)\n## Participant selection and newly discovered cases\n## Variables and outcomes\n## Training/validation split\n# Machine Learning Approach\n## Five supervised algorithms\n## Balancing techniques\n## Comparison with medical screening protocol\n# Results and Discussion\n## Sensitivity–specificity trade-off\n## Random Forest performance vs protocols\n## Implications for precision and cost-effective screening\n## Need for additional features and further studies","[{\"question\":\"Why is improving hypertension detection considered urgent?\",\"answer\":\"High blood pressure has major effects on cardiovascular events and mortality. Current screening programs still fail to identify people at higher risk, making better detection strategies necessary.\"},{\"question\":\"What data were used to train and evaluate the machine learning models?\",\"answer\":\"The study analyzed questionnaire data collected during World Hypertension Day campaigns from 2015 to 2019, selecting 20,206 participants after excluding those with previously diagnosed high blood pressure.\"},{\"question\":\"How did the machine learning models perform compared with medical screening protocols?\",\"answer\":\"Performance depends on the sensitivity–specificity trade-off; no algorithm clearly outperformed all others. Random Forest showed comparatively useful results (about 0.818 sensitivity and 0.629 specificity) versus the medical protocol’s higher sensitivity but much lower specificity.\"}]","Machine Learning in Hypertension Detection - A Study on World Hypertension Day Data | PDF",1785817737,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-hypertension-detection-a-study-on-world-hypertension-day-data","",{"@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/machine-learning-in-hypertension-detection-a-study-on-world-hypertension-day-data/123631/",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},"Why is improving hypertension detection considered urgent?","Question",{"text":75,"@type":76},"High blood pressure has major effects on cardiovascular events and mortality. Current screening programs still fail to identify people at higher risk, making better detection strategies necessary.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data were used to train and evaluate the machine learning models?",{"text":80,"@type":76},"The study analyzed questionnaire data collected during World Hypertension Day campaigns from 2015 to 2019, selecting 20,206 participants after excluding those with previously diagnosed high blood pressure.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the machine learning models perform compared with medical screening protocols?",{"text":84,"@type":76},"Performance depends on the sensitivity–specificity trade-off; no algorithm clearly outperformed all others. Random Forest showed comparatively useful results (about 0.818 sensitivity and 0.629 specificity) versus the medical protocol’s higher sensitivity but much lower specificity.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]