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Abstract：
Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.
摘要：
心血管疾病（CVDs）占全球死亡率的很大一部分，强调需要有效的战略。这项研究的重点是心肌梗塞，肺血栓栓塞症,和主动脉瓣狭窄，旨在授权医疗从业者提供知情决策和及时干预的工具。根据圣玛丽亚医院的数据，我们的方法结合了探索性数据分析(EDA)和预测性机器学习(ML)模型，由跨行业数据挖掘标准流程(CRISP-DM)方法指导。EDA揭示了心血管疾病特有的复杂模式和关系。ML模型的精度达到80%以上，提供一个13分钟的窗口来预测心肌缺血事件并积极干预。本文介绍了增强医疗策略的实时数据和预测能力的概念证明。
