Predictive Modeling of Heart Attack Risk in China using Lifestyle, Clinical, and Socioeconomic Indicators
Abstract
Cardiovascular disease remains the leading cause of death globally, with heart attacks representing a significant proportion. This study explores the predictive modeling of heart attack risks in the Chinese population using a large-scale dataset of over 239,000 individuals. Key variables span lifestyle, clinical, environmental, and socioeconomic dimensions. By applying supervised machine learning techniques, the study aims to identify critical factors contributing to heart attack incidence and demonstrate the potential for data-driven public health intervention.
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Introduction
Heart attacks, or myocardial infarctions, present a major public health challenge in China, driven by changing lifestyles, aging populations, and environmental pressures. The integration of big data analytics in healthcare offers a transformative approach to identifying at-risk individuals before symptoms manifest. This paper leverages a wide-ranging dataset to build predictive models and explore actionable insights from demographic, clinical, and behavioral attributes.
Conclusion
This study demonstrates the utility of machine learning for heart attack risk prediction in China. Key lifestyle and clinical factors were reaffirmed as critical indicators, alongside new insights into the role of environmental and socioeconomic influences. These models can inform public health strategies and individual screening programs, aiding in early intervention and resource allocation.