[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116872-en":3,"doc-seo-116872-105":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},116872,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine learning and physical based modeling for cardiac hypertrophy","Predicting the long-term expansion and remodeling of the left ventricle in cardiac hypertrophy is clinically valuable yet challenging. The study presents machine learning models (random forests, gradient boosting, and neural networks) trained on patient medical history and cardiac health measurements, alongside a physics-based finite element model simulating hypertrophy development. Model forecasts over six years show similar trends, with the finite element approach offering greater accuracy and the machine learning approach enabling faster clinical use.","Machine learning and physical based modeling for cardiac hypertrophy  \nMilićević, B., Milošević, M., Simić, V., Preveden, A., Velicki, L., Jakovljević, Đ., Bosnić, Z., Pičulin, M., Žunkovič, B., Kojić, M. & Filipović, N .  \nPublished PDF deposited in Coventry University’s Repository  \nOriginal citation:  \nMilićević, B, Milošević, M, Simić, V, Preveden, A, Velicki, L, Jakovljević, Đ, Bosnić, Z, Pičulin, M, Žunkovič, B, Kojić, M & Filipović, N 2023, 'Machine learning and physical based modeling for cardiac hypertrophy', Heliyon, vol. 9, no. 6, e16724 .  \n[https://dx.doi.org/10.1016/j.heliyon.2023.e16724](https://dx.doi.org/10.1016/j.heliyon.2023.e16724)  \n[DOI 10.1016/j.heliyon.2023.e16724](DOI 10.1016/j.heliyon.2023.e16724)[ ](DOI 10.1016/j.heliyon.2023.e16724)[ISSN 2405-8440](ISSN 2405-8440)  \nPublisher: Elsevier  \nThis is an open access article under the CC BY-NC-ND license ( [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/))  \nHeliyon 9 (2023) e16724  \nContents lists available at ScienceDirect  \nHeliyon  \njournal [homepage: www.cell.com/heliyon](homepage: www.cell.com/heliyon)  \n| Machine learning and physical based modeling for cardiac hypertrophy\u003Cbr>Bogdan Mili´cevi´c a, b, Miljan Miloˇsevi´cb, c, d, Vladimir Simi´cb, c, Andrej Preveden e, Lazar Velickie, Đorđe Jakovljevi´cf, g, Zoran Bosni´ch, Matej Piˇculinh,\u003Cbr>Bojan ˇZunkoviˇch, Miloˇs Koji´cb, i,j, Nenad Filipovi´c a, b, *\u003Cbr>a Faculty of Engineering, University of Kragujevac, Kragujevac 34000, Serbia b Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia c Institute for Information Technologies, University of Kragujevac, Kragujevac 34000, Serbia d Belgrade Metropolitan University, Belgrade 11000, Serbia\u003Cbr>e Faculty of Medicine, University of Novi Sad, Serbia and Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia f Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK g Faculty of Health and Life Sciences, Coventry University, Coventry, UK\u003Cbr>h University of Ljubljana, Faculty of Computer and Information Science, Veˇcna Pot 113, Ljubljana, Slovenia\u003Cbr>i Serbian Academy of Sciences and Arts, Belgrade 11000, Serbia j Houston Methodist Research Institute, Houston TX 77030, USA |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Finite element analysis Machine learning\u003Cbr>Left ventricle mode Cardiac hypertrophy Disease progress tracking |  | Background and objective: Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful.\u003Cbr>Methods: In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient’s medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy.\u003Cbr>Results: Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results.\u003Cbr>Conclusions: The finite element model is much slower, but it’s more accurate compared to the machine learning model since it’s based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast","cbCaifL3orjUjkQg","https://ap.wps.com/l/cbCaifL3orjUjkQg","pdf",6664867,1,16,"English","en",105,"# Introduction\n## Machine learning vs physical-based modeling\n## Methods and modeling approach\n## Results and six-year forecasting\n## Conclusions and future work","[{\"question\":\"What is the main goal of the study on cardiac hypertrophy?\",\"answer\":\"To predict the long-term expansion and remodeling of the left ventricle, enabling disease monitoring with clinically useful forecasts.\"},{\"question\":\"Which machine learning methods are used to track hypertrophy?\",\"answer\":\"The study uses random forests, gradient boosting, and neural networks trained on patient medical history and current cardiac health.\"},{\"question\":\"How do the machine learning and physical-based finite element models compare?\",\"answer\":\"Both produce similar six-year evolution results; the finite element model is slower but more accurate because it follows physical laws, while the machine learning model is faster but can be less trustworthy in some cases.\"}]","Machine learning and physical based modeling for cardiac hypertrophy | PDF",1785672166,40,{"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-and-physical-based-modeling-for-cardiac-hypertrophy","",{"@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-and-physical-based-modeling-for-cardiac-hypertrophy/116872/",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","2026-08-02",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 is the main goal of the study on cardiac hypertrophy?","Question",{"text":75,"@type":76},"To predict the long-term expansion and remodeling of the left ventricle, enabling disease monitoring with clinically useful forecasts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used to track hypertrophy?",{"text":80,"@type":76},"The study uses random forests, gradient boosting, and neural networks trained on patient medical history and current cardiac health.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the machine learning and physical-based finite element models compare?",{"text":84,"@type":76},"Both produce similar six-year evolution results; 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