Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting

Mingyang Feng, Xiaosong Wang, Zhiming Zhao, Chufeng Jiang, Jize Xiong, Ning Zhang
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引用次数: 0

Abstract

Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.
利用梯度提升技术增强心脏病发作预测能力
心脏病发作预测是心血管医疗保健的重要组成部分,其目的是识别高危人群,以便及时干预,改善患者预后。尽管预测建模技术取得了重大进展,但仍存在一些挑战,包括算法限制、可解释性问题、数据依赖性和可扩展性问题。这些挑战突出表明,我们需要能够有效处理复杂医疗数据的稳健、可解释和可推广的预测模型。在本研究中,我们提出了一种利用梯度提升算法(XGBoost)进行心脏病分析和预测的新方法。我们对心脏病数据集进行了全面分析,采用了严格的数据预处理、特征选择和超参数优化技术,开发出了高精度、可解释的预测模型。我们的研究结果表明,XGBoost 算法能从医疗数据中捕捉到复杂的模式,并在各种指标上实现卓越的预测性能。所提出的模型解决了心脏病发作预测中的现有难题,为提高心血管医疗保健效果提供了一个前景广阔的解决方案。
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