用机器学习分析6.8万份数据，找出心脏病关键风险因素。
Cardiovascular Disease Prediction using Machine Learning: A Comparative Analysis
- 基于6.8万条数据，用统计与模型识别出年龄、血压、胆固醇是主要风险
- CatBoost模型准确率达73.4%，在概率预测上表现最佳
- 发现吸烟和饮酒反向关联，提示数据可能存在异常，适合医疗研究者参考
心血管疾病（CVD）是全球死亡主因，占总死亡人数的31%。本研究使用包含68,119条记录的心血管疾病数据集，分析数值特征（年龄、身高、体重、血压、BMI）和类别特征（性别、胆固醇水平、血糖、吸烟、饮酒、体力活动）对CVD发生的影响。通过t检验、卡方检验和ANOVA进行统计分析，发现老年人群、高血压、体重较高及胆固醇异常与CVD显著相关，而体力活动为保护性因素。逻辑回归模型表明年龄、血压和胆固醇是主要风险因素，但吸烟和饮酒呈现意外负相关，暗示可能存在数据问题。模型性能对比显示，CatBoost表现最优，准确率为0.734，期望校准误差（ECE）为0.0064，贝叶斯分数（Brier score）为0.1824，尤其在概率预测方面优势明显。数据存在异常值和分布偏斜问题，提示需改进预处理以提升预测可靠性。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVDs) are a main cause of mortality globally, accounting for 31% of all deaths. This study involves a cardiovascular disease (CVD) dataset comprising 68,119 records to explore the influence of numerical (age, height, weight, blood pressure, BMI) and categorical gender, cholesterol, glucose, smoking, alcohol, activity) factors on CVD occurrence. We have performed statistical analyses, including t-tests, Chi-square tests, and ANOVA, to identify strong associations between CVD and elderly people, hypertension, higher weight, and abnormal cholesterol levels, while physical activity (a protective factor). A logistic regression model highlights age, blood pressure, and cholesterol as primary risk factors, with unexpected negative associations for smoking and alcohol, suggesting potential data issues. Model performance comparisons reveal CatBoost as the top performer with an accuracy of 0.734 and an ECE of 0.0064 and excels in probabilistic prediction (Brier score = 0.1824). Data challenges, including outliers and skewed distributions, indicate a need for improved preprocessing to enhance predictive reliability.
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