[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127755-en":3,"doc-seo-127755-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127755,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank","Hypertension elevates the risk of cardiovascular diseases including stroke, heart attack, heart failure, and kidney disease, driving global morbidity and premature mortality. Existing statistical and machine learning models for hypertension rarely include genetic liabilities and seldom quantify their added predictive value. This study develops hypertension classification models and assesses how genetic liabilities tied to CVD risk factors influence risk, comparing random forest and neural network approaches. Evaluation uses AUC, calibration, and net reclassification improvement.","Article  \nUsing Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank  \nGideon MacCarthy 1 and Raha Pazoki 1,2, *  \nCitation: MacCarthy, G.; Pazoki, R. Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank. J. Clin. Med. 2024, 13, 2955. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jcm13102955  \nAcademic Editor: Andrea Dell’Amore  \nReceived: 18 March 2024  \nRevised: 1 May 2024  \nAccepted: 7 May 2024  \nPublished: 17 May 2024  \nCopyright: © 2024 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 Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK  \n2 MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Campus, Norfolk Place, Imperial College London, London W2 1PG, UK  \n* Correspondence: [raha.pazoki@brunel.ac.uk](raha.pazoki@brunel.ac.uk)  \nAbstract: Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS) . Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99) . This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL) . Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.  \nKeywords: the receiver operation characteristic (ROC); area under the curve (AUC)  \n1. Introduction  \nApproximately 1.28 billion people aged 30 to 79 have hypertension worldwide [1], and it continues to rise globally, causing a significant socioeconomic burden due to low awareness and poor control [2] . Hypertension significantly increases the risk of cardiovascular diseases (CVD), including stroke, heart attack, heart failure, and kidney disease, contributing to the global disease burden and premature mortality [1,3,4] .  \nEvery year the burden of hypertension and related CVD is incre","cbCaihGAWrNQLWOz","https://ap.wps.com/l/cbCaihGAWrNQLWOz","pdf",2491406,3,1,20,"English","en",105,"# Abstract\n# Introduction\n## Global and UK burden of hypertension\n## Clinical guidelines and genetic components\n## Genetic findings from GWAS\n## Prior prediction models and research gap\n# Materials and Methods\n## Study participants and datasets\n## Construction of genetic liabilities\n## Model development and feature selection\n## Evaluation metrics","[{\"question\":\"What is the purpose of including genetic liabilities in hypertension classification?\",\"answer\":\"The study evaluates whether genetic liabilities for CVD-related risk factors improve hypertension classification beyond baseline characteristics. It specifically tests added value in model performance metrics.\"},{\"question\":\"Which machine learning models were compared in the study?\",\"answer\":\"The study compares random forest and neural network classifiers, both with and without genetic liabilities and with feature selection applied in different stages.\"},{\"question\":\"What data and evaluation approach were used?\",\"answer\":\"The research uses 244,718 European participants split into training and testing sets. Models are assessed using AUC, calibration, and net reclassification improvement on the testing set.\"}]","Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank | PDF",1785941432,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"using-machine-learning-to-evaluate-the-value-of-genetic-liabilities-in-the-classification-of-hypertension-within-the-uk-biobank","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/using-machine-learning-to-evaluate-the-value-of-genetic-liabilities-in-the-classification-of-hypertension-within-the-uk-biobank/127755/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the purpose of including genetic liabilities in hypertension classification?","Question",{"text":76,"@type":77},"The study evaluates whether genetic liabilities for CVD-related risk factors improve hypertension classification beyond baseline characteristics. It specifically tests added value in model performance metrics.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were compared in the study?",{"text":81,"@type":77},"The study compares random forest and neural network classifiers, both with and without genetic liabilities and with feature selection applied in different stages.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and evaluation approach were used?",{"text":85,"@type":77},"The research uses 244,718 European participants split into training and testing sets. 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