{"title":"利用先进的机器学习技术,通过血液检验加强代谢综合征检测","authors":"Petros Paplomatas, Dimitris Rigas, Athanasia Sergounioti, Aris Vrahatis","doi":"10.3390/eng5030075","DOIUrl":null,"url":null,"abstract":"The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health.","PeriodicalId":502660,"journal":{"name":"Eng","volume":"52 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning\",\"authors\":\"Petros Paplomatas, Dimitris Rigas, Athanasia Sergounioti, Aris Vrahatis\",\"doi\":\"10.3390/eng5030075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health.\",\"PeriodicalId\":502660,\"journal\":{\"name\":\"Eng\",\"volume\":\"52 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Eng\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/eng5030075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eng","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/eng5030075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
代谢综合征(MetS)是一种与心血管疾病、中风和 2 型糖尿病风险升高有关的严重疾病,其发病率的不断上升凸显了对有效诊断工具的迫切需求。MetS是一种多方面的健康问题,与心脏病和其他严重健康并发症的风险增加有关,本研究仔细研究了16种不同的机器学习(ML)模型在预测MetS方面的有效性。我们的研究利用希腊阿姆菲萨综合医院实验室信息系统提供的从 2017 年到 2022 年不平衡血液检测结果的综合、未公开数据集,着手采用一种新方法来加强 MetS 诊断。通过利用先进的 ML 技术,我们旨在利用非侵入性血液检验数据更准确地预测 MetS,从而减少对更具侵入性诊断方法的依赖。我们方法的核心是应用博尔达计数法,这是一种用于完善数据集的创新技术。这一过程根据主要 ML 模型的性能确定最相关变量的优先次序,确保分析更有针对性、更有效。我们选择的模型涵盖了一系列广泛的 ML 技术,可以全面比较它们在识别 MetS 方面的预测能力。这项研究不仅揭示了每种 ML 模型在预测 MetS 方面的独特优势,还揭示了这些方法在更广泛的健康诊断领域中的巨大潜力。从我们的分析中获得的见解对于制定更有效的代谢综合征管理和预防策略至关重要,从而解决了公共卫生领域的一个重大问题。
Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning
The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health.