Rejath Jose, Anvin Thomas, Jennifer Guo, Robert Steinberg, Milan Toma
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引用次数: 0
摘要
冠状动脉疾病(CAD)是全球普遍存在的健康问题,也是导致全球死亡的主要原因。准确及时的诊断对于有效控制疾病和改善患者预后至关重要。在这项研究中,我们对基于机器学习(ML)的检测和诊断 CAD 的方法进行了比较分析。研究使用了来自 UCI ML 库的 918 个实例数据集,其中包括 11 个典型的危险因素和 CAD 预测因子。研究在 Google Colaboratory 和 PyCaret 中部署了 ML 模型,测试它们在诊断 CAD 方面的功效。我们的研究详细概述了这些 ML 方法、它们的优势和局限性,强调了这些算法彻底改变 CAD 诊断和治疗的潜力。本研究的总体目标是创建一个模型,根据患者病史的不同参数预测是否存在或有可能存在 CAD。研究结果包括展示的逻辑回归模型,该模型被证明特别有效,其曲线下面积为 0.88,表明该模型具有很强的区分有无 CAD 患者的能力,并能成功识别 CAD 的临床关键特征,如是否存在劳累性心绞痛和胸痛。这项研究强调了在该领域开展进一步研究的重要性,以将 ML 确立为现代医疗诊断的基石。
Evaluating machine learning models for prediction of coronary artery disease
Coronary artery disease (CAD) is a prevailing global health issue and a leading cause of death worldwide. Its accurate and timely diagnosis is crucial for effectively managing the disease and improving patient outcomes. In this study, we conducted a comparative analysis of machine learning (ML)-based approaches to detect and diagnose CAD. A dataset of 918 instances from the UCI ML repository, comprising 11 typical risk factors and CAD predictors, was used for this investigation. The study deployed ML models in Google Colaboratory and PyCaret, testing their efficacy in diagnosing CAD. Our study provides a detailed overview of these ML methodologies, their strengths, and limitations, underscoring the potential of these algorithms to revolutionize CAD diagnosis and treatment. The overall goal of the study is to create a model that can predict the presence or chance of presence of CAD based on different parameters of the patient’s history. Findings include the showcased logistic regression model, which was proven to be particularly effective, with an area under curve of 0.88, indicating a high ability to differentiate between patients with and without CAD, and a successful ability to identify clinically key features of CAD such as the presence of exertional angina and chest pain. This study emphasizes the importance of further research in this field to establish ML as a cornerstone of modern healthcare diagnostics.