基于XGBoost和SHAP的心脏病影响因素分析

Analysis of influencing factors of heart disease based on XGBoost and SHAP

心脏病是一种常见的严重疾病,严重影响人们的健康和生活质量。近年来,心脏病的预测和分析研究受到了广泛关注。随着机器学习和数据科学的发展,越来越多的研究采用这些方法构建心脏病的预测模型和分析方法。文章提出一种基于数据分析和机器学习算法的心脏病预测模型,通过构建逻辑回归模型评估了影响心脏病的因素,并进行了实证研究。结果显示,构建的模型在心脏病预测影响因素方面表现良好,具有稳定性。

Heart disease is a common and serious disease that seriously affects people's health and quality of life.In recent years,the prediction and analysis of heart disease have received widespread attention.With the development of machine learning and data science,more and more research is using these methods to construct predictive models and analysis methods for heart disease.The article proposes a heart disease prediction model based on data analysis and machine learning algorithms,evaluates the factors affecting heart disease by constructing a logistic regression model,and conducts empirical research.The results show that the constructed model performs well in predicting the influencing factors of heart disease and has stability.