[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124341-en":3,"doc-seo-124341-105":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},124341,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","MACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE - ADVANCING CARDIAC ARRHYTHMIA CLASSIFICATION IN HEALTHCARE ANALYTICS - Work Project","Cardiac arrhythmias are a leading global cause of disease that requires rapid, accurate diagnosis. The shift from manual ECG interpretation to machine learning aims to improve efficiency and detection quality, yet prior research highlights long training times and limited practical fit. Three prototypes were developed and tested, then the best-performing model was further optimized. The optimized CNN reached an overall classification accuracy of 98.53%. Applicability was evaluated in practical context and compared with existing approaches, achieving above-average results.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Management from the Nova School of Business and Economics.  \nMACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE: ADVANCING CARDIAC ARRHYTHMIA CLASSIFICATION IN HEALTHCARE ANALYTICS  \nALEXANDER MEHLER  \nWork project carried out under the supervision of:  \nRodrigo Belo  \n17/01/2024  \nAbstract (100 words maximum)  \nCardiac arrhythmias, a global leading disease cause, necessitate rapid, efficient diagnosis. Shifting from traditional manual electrocardiogram analysis to machine learning approaches offers enhanced efficiency and accuracy in detection. However, literature research has shown that long training times and a lack of practical suitability have made implementation difficult to date. Three prototypes were developed and tested; the results were then used to optimize the most promising model further. The optimized CNN achieved an overall classification accuracy of 98.53% . The results are tested for their applicability in a practical context, evaluated, and compared against existing approaches, resulting in above-average classification outcomes.  \nKeywords:  \nPredictive Modeling; Convolutional Neural Networks; Deep Learning; Arrhythmia; Classification; Hybrid Models  \nThis work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) .  \n1. Introduction  \nEvery year, cardiovascular diseases cause almost 20 million deaths worldwide (O'Riordan 2022) . According to a study by the American Heart Association, this figure is expected to increase by 23.56% by 2030, bringing the annual death toll to 23.6 million per year (Angell et al. 2020) . Making this the leading cause of death worldwide shows that cardiovascular diseases are omnipresent in our daily life, known and experienced in a family context or from personal experience (O'Riordan 2022) . One form of these cardiovascular diseases is arrhythmia. Although this type of disease is well known, one may have difficulty explaining what it implies. So, the question arises: what is arrhythmia, and how can it be treated effectively? According to the medical dictionary, \"Arrhythmia\" refers to a group of conditions reflecting electrical impulses that vary from the typical sequence, causing the heart to beat erratically (Cambridge Heart Clinic 2019). Arrhythmia occurs in all age groups (Khanal et al. 2023) . Therefore, early detection and treatment are essential to survive arrhythmia, mainly because symptoms often occur unnoticed initially. The diagnosis is made using ECG data, which, because of their noninvasive nature, acts as a convenient diagnostic tool.  \nDue to its increasing prevalence, research on the detection and classification of cardiovascular diseases has gained more interest over the past decades, especially in machine learning (Chenet al. 2022) . The emergence of computational resources and the development of intelligent devices, achieving continuous and remote monitoring of ECG, allows researchers to believe that diagnostic systems based on machine learning can minimize the burden of instinctive uncertainty of experts, potentially leading to a misdiagnosis (Javaid et al. 2022) . Therefore, a shift towards a less time-consuming and laborious option is necessary (Appendix 1) . A study by Sturman et al. (2020) shows that classification using deep learning can generate higher accuracy and efficiency than a cardiologist's classification.  \nThis study focuses on machine and deep learning models and their underlying mechanisms: the development of a vigorous method that can be used in practice. It is well known that modern machine learning approaches have the potential to recognize precise patterns in ECG data due to their robustness and generaliza","cbCaivOV5D48zPdJ","https://ap.wps.com/l/cbCaivOV5D48zPdJ","pdf",1076914,1,42,"English","en",105,"# Introduction\n## Clinical background and motivation\n## Research gap and study goal\n## Thesis structure\n# Literature Review\n## Traditional vs deep learning approaches","[{\"question\":\"What problem does the work project address in healthcare analytics?\",\"answer\":\"It addresses the need for faster, more accurate detection of cardiac arrhythmias by moving from manual ECG analysis to machine learning methods for classification.\"},{\"question\":\"How were the machine learning models developed and evaluated?\",\"answer\":\"Three prototypes were developed and tested; the most promising model was then optimized further. The optimized CNN achieved 98.53% overall classification accuracy and was evaluated for practical applicability and compared with existing approaches.\"},{\"question\":\"Why is early arrhythmia detection considered essential?\",\"answer\":\"Symptoms often occur unnoticed initially, making early detection and treatment crucial for survival. Diagnosis relies on ECG data due to its noninvasive diagnostic value.\"}]","MACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE - ADVANCING CARDIAC ARRHYTHMIA CLASSIFICATION IN HEALTHCARE ANALYTICS - Work Project | PDF",1785821718,106,{"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-deep-learning-in-healthcare-advancing-cardiac-arrhythmia-classification-in-healthcare-analytics-work-project","",{"@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-deep-learning-in-healthcare-advancing-cardiac-arrhythmia-classification-in-healthcare-analytics-work-project/124341/",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},"What problem does the work project address in healthcare analytics?","Question",{"text":75,"@type":76},"It addresses the need for faster, more accurate detection of cardiac arrhythmias by moving from manual ECG analysis to machine learning methods for classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models developed and evaluated?",{"text":80,"@type":76},"Three prototypes were developed and tested; the most promising model was then optimized further. The optimized CNN achieved 98.53% overall classification accuracy and was evaluated for practical applicability and compared with existing approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is early arrhythmia detection considered essential?",{"text":84,"@type":76},"Symptoms often occur unnoticed initially, making early detection and treatment crucial for survival. 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