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Add CardioAI project with usage instructions

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- Add comprehensive README.md with setup and usage instructions
- Add .env.example template (sanitized, no real API keys)
- Add root-level .gitignore to exclude .env and generated files
- Add all project modules (dashboard, predictor)
- Add data file and requirements.txt
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zhenchuan199
2026-04-02 20:11:24 +08:00
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# DeepSeek API 配置
BASE_URL=https://api.deepseek.com/v1
DEEPSEEK_API_KEY=your_api_key_here
MODEL_NAME=deepseek-reasoner
# Flask 配置
FLASK_ENV=development
FLASK_DEBUG=True
# 数据文件路径
DATA_PATH=./data/心血管疾病.xlsx

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# Environment
.env
*.pyc
__pycache__/
# IDE
.idea/
# Model files (generated)
*.pkl
# OS
.DS_Store

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# CardioAI - 心血管疾病智能辅助系统
多模块应用，集成了数据可视化、机器学习预测和AI问答功能。
## 项目结构
```
CardioAI/
├── data/ # 数据文件目录
│ └── 心血管疾病.xlsx # 心血管疾病数据集
├── module1_dashboard/ # 模块1: Streamlit 交互式仪表盘
│ └── cardio_dashboard.py
├── module2_predictor/ # 模块2: XGBoost 风险预测模型
│ ├── train_and_save.py # 模型训练脚本
│ ├── cardio_predictor_model.pkl # 训练好的模型
│ ├── app.py # Flask API 服务
│ └── templates/
│ └── index.html # 预测前端页面
├── requirements.txt # 项目依赖
├── .env.example # 环境变量模板
└── .gitignore # Git 忽略文件
```
## 环境配置
### 1. 创建 conda 环境
```bash
conda create -n cardioenv python=3.10
conda activate cardioenv
```
### 2. 安装依赖
```bash
cd F:\My_Git_Project\CardioAI
pip install -r requirements.txt
```
### 3. 配置环境变量
复制 `.env.example` 为 `.env`，并填入您的 API Key：
```bash
copy .env.example .env
```
编辑 `.env` 文件，填入您的 DeepSeek API Key。
## 模块说明
### 模块1: 交互式仪表盘 (Streamlit)
心血管数据的交互式可视化界面。
**启动命令:**
```bash
cd F:\My_Git_Project\CardioAI
streamlit run module1_dashboard/cardio_dashboard.py
```
**功能特性:**
- 年龄范围筛选
- 性别和心血管疾病状态筛选
- 统计数据展示
- BMI分布可视化
### 模块2: 心血管风险预测模型 (Flask + XGBoost)
基于 XGBoost 的心血管疾病风险预测 API。
**训练模型:**
```bash
cd F:\My_Git_Project\CardioAI
python module2_predictor/train_and_save.py
```
**启动预测服务:**
```bash
cd F:\My_Git_Project\CardioAI\module2_predictor
set FLASK_APP=app.py
flask run --host=0.0.0.0 --port=5000
```
**API 接口:**
- `POST /predict_cardio` - 提交11个特征值，返回预测概率和结果
## 依赖说明
- pandas, openpyxl - 数据处理
- numpy, scikit-learn - 数值计算
- xgboost, joblib - 机器学习
- streamlit, plotly - 数据可视化
- Flask - Web 服务
- python-dotenv - 环境变量
- langchain-openai, dashscope, requests - AI 集成
## 注意事项
1. 数据文件路径可通过 `.env` 中的 `DATA_PATH` 配置
2. 确保 `.env` 文件不提交到版本库（已加入 .gitignore）
3. 使用前请确保已正确配置 DeepSeek API Key

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"""
CardioAI 模块1: 交互式仪表盘
心血管疾病数据可视化系统 - 美化版
"""
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from pathlib import Path
# ==================== 页面配置 ====================
st.set_page_config(
page_title="CardioAI 心血管疾病分析",
page_icon="❤️",
layout="wide",
initial_sidebar_state="expanded"
)
# ==================== 自定义CSS样式 ====================
st.markdown("""
<style>
/* 全局样式 */
.main {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
padding: 2rem;
}
/* 标题样式 */
.main-title {
text-align: center;
background: linear-gradient(90deg, #ff6b6b, #feca57);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-size: 3rem !important;
font-weight: 800 !important;
margin-bottom: 0.5rem !important;
text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
}
.sub-title {
text-align: center;
color: #666;
font-size: 1.1rem;
margin-bottom: 2rem;
}
/* 侧边栏样式 */
.sidebar .sidebar-content {
background: linear-gradient(180deg, #1e3c72 0%, #2a5298 100%);
}
/* 指标卡片样式 */
.metric-card {
background: white;
border-radius: 16px;
padding: 1.5rem;
box-shadow: 0 10px 40px rgba(0,0,0,0.1);
transition: transform 0.3s ease, box-shadow 0.3s ease;
border: 1px solid rgba(255,255,255,0.2);
}
.metric-card:hover {
transform: translateY(-5px);
box-shadow: 0 20px 60px rgba(0,0,0,0.15);
}
.metric-value {
font-size: 2.5rem;
font-weight: 700;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.metric-label {
color: #666;
font-size: 0.9rem;
font-weight: 500;
}
/* 图表容器样式 */
.chart-container {
background: white;
border-radius: 16px;
padding: 1.5rem;
box-shadow: 0 4px 20px rgba(0,0,0,0.08);
margin-bottom: 1.5rem;
}
/* 区块标题样式 */
.section-title {
background: linear-gradient(90deg, #667eea, #764ba2);
color: white !important;
padding: 0.8rem 1.5rem;
border-radius: 12px;
font-size: 1.2rem;
font-weight: 600;
margin-bottom: 1.5rem;
box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4);
}
/* 数据表格样式 */
.dataframe {
font-size: 0.85rem;
}
.dataframe th {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
font-weight: 600;
padding: 12px;
}
.dataframe td {
padding: 10px;
border-bottom: 1px solid #eee;
}
.dataframe tr:hover {
background-color: #f5f7fa;
}
/* 状态标签 */
.status-safe {
background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
color: white;
padding: 0.3rem 0.8rem;
border-radius: 20px;
font-size: 0.8rem;
font-weight: 600;
}
.status-risk {
background: linear-gradient(135deg, #ff416c 0%, #ff4b2b 100%);
color: white;
padding: 0.3rem 0.8rem;
border-radius: 20px;
font-size: 0.8rem;
font-weight: 600;
}
/* 进度条样式 */
.stProgress > div > div {
background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
border-radius: 10px;
}
/* 侧边栏筛选器样式 */
.sidebar-filter {
background: rgba(255,255,255,0.1);
padding: 1rem;
border-radius: 12px;
margin-bottom: 1rem;
}
/* 分隔线样式 */
hr {
border: none;
height: 2px;
background: linear-gradient(90deg, transparent, #667eea, transparent);
margin: 2rem 0;
}
/* 信息卡片 */
.info-box {
background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
border-left: 4px solid #667eea;
padding: 1rem 1.5rem;
border-radius: 0 12px 12px 0;
margin: 1rem 0;
}
/* 按钮样式 */
.stButton > button {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
border-radius: 25px;
padding: 0.6rem 2rem;
font-weight: 600;
transition: all 0.3s ease;
}
.stButton > button:hover {
transform: scale(1.05);
box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4);
}
</style>
""", unsafe_allow_html=True)
# ==================== 常量定义 ====================
CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
DATA_PATH = CODE_ROOT / "data" / "心血管疾病.xlsx"
# 配色方案
COLORS = {
'primary': ['#667eea', '#764ba2', '#f093fb', '#f5576c'],
'safe': '#2ecc71',
'risk': '#e74c3c',
'gradient': ['#667eea', '#764ba2'],
'bmi': ['#3498db', '#2ecc71', '#f39c12', '#e74c3c']
}
# ==================== 数据加载函数 ====================
@st.cache_data(show_spinner=False)
def load_data(file_path: Path) -> pd.DataFrame:
"""加载数据，支持Excel格式"""
try:
df = pd.read_excel(file_path, engine='openpyxl')
return df
except Exception as e:
st.error(f"数据加载失败: {e}")
return pd.DataFrame()
@st.cache_data(show_spinner=False)
def clean_and_engineer_features(df: pd.DataFrame) -> pd.DataFrame:
"""数据清洗和特征工程"""
df = df.copy()
# 1. 特征工程
df['age_years'] = (df['age'] / 365).round().astype(int)
df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
# 2. 异常值处理
df = df[df['ap_lo'] < df['ap_hi']]
df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
# 3. 类别转换
cholesterol_map = {1: '正常', 2: '偏高', 3: '非常高'}
gluc_map = {1: '正常', 2: '偏高', 3: '非常高'}
df['cholesterol_cat'] = df['cholesterol'].map(cholesterol_map)
df['gluc_cat'] = df['gluc'].map(gluc_map)
# 4. BMI分类
def categorize_bmi(bmi):
if bmi < 18.5:
return '体重过低'
elif bmi < 25:
return '体重正常'
elif bmi < 30:
return '超重'
else:
return '肥胖'
df['bmi_category'] = df['bmi'].apply(categorize_bmi)
return df
# ==================== UI组件 ====================
def render_header():
"""渲染页面头部"""
st.markdown('<h1 class="main-title">❤️ CardioAI</h1>', unsafe_allow_html=True)
st.markdown('<p class="sub-title">心血管疾病智能分析系统 | 数据驱动的健康洞察</p>', unsafe_allow_html=True)
st.markdown("<hr>", unsafe_allow_html=True)
def create_sidebar(df: pd.DataFrame) -> dict:
"""创建美观的侧边栏"""
with st.sidebar:
st.markdown("### 🎛️ 数据筛选器")
st.markdown("---")
# 年龄范围
with st.container():
st.markdown("**📅 年龄范围**")
age_range = st.slider(
"",
min_value=int(df['age_years'].min()),
max_value=int(df['age_years'].max()),
value=(int(df['age_years'].min()), int(df['age_years'].max())),
key="age_slider"
)
st.markdown("<br>", unsafe_allow_html=True)
# 性别选择
with st.container():
st.markdown("**👤 性别**")
gender_options = st.multiselect(
"",
options=[1, 2],
default=[1, 2],
format_func=lambda x: "👩 女性" if x == 1 else "👨 男性",
key="gender_select"
)
st.markdown("<br>", unsafe_allow_html=True)
# 心血管疾病状态
with st.container():
st.markdown("**🏥 心血管健康状态**")
cardio_options = st.multiselect(
"",
options=[0, 1],
default=[0, 1],
format_func=lambda x: "✅ 健康" if x == 0 else "⚠️ 有风险",
key="cardio_select"
)
st.markdown("---")
# 数据统计
st.markdown("### 📊 数据概览")
st.metric("总记录数", f"{len(df):,}")
st.metric("平均BMI", f"{df['bmi'].mean():.1f}")
st.metric("平均年龄", f"{df['age_years'].mean():.1f} 岁")
return {
'age_range': age_range,
'gender': gender_options,
'cardio': cardio_options
}
def apply_filters(df: pd.DataFrame, filters: dict) -> pd.DataFrame:
"""应用筛选条件"""
return df[
(df['age_years'] >= filters['age_range'][0]) &
(df['age_years'] <= filters['age_range'][1]) &
(df['gender'].isin(filters['gender'])) &
(df['cardio'].isin(filters['cardio']))
]
def render_metrics(filtered_df: pd.DataFrame, total_count: int):
"""渲染指标卡片"""
st.markdown('<div class="section-title">📊 关键指标</div>', unsafe_allow_html=True)
col1, col2, col3, col4 = st.columns(4)
with col1:
st.markdown(f"""
<div class="metric-card">
<div class="metric-label">📋 筛选记录数</div>
<div class="metric-value">{len(filtered_df):,}</div>
<div style="color: #999; font-size: 0.8rem;">占比 {(len(filtered_df)/total_count*100):.1f}%</div>
</div>
""", unsafe_allow_html=True)
with col2:
risk_rate = (filtered_df['cardio'].sum() / len(filtered_df) * 100) if len(filtered_df) > 0 else 0
st.markdown(f"""
<div class="metric-card">
<div class="metric-label">⚠️ 风险率</div>
<div class="metric-value">{risk_rate:.1f}%</div>
<div style="color: #999; font-size: 0.8rem;">心血管疾病患者占比</div>
</div>
""", unsafe_allow_html=True)
with col3:
avg_age = filtered_df['age_years'].mean() if len(filtered_df) > 0 else 0
st.markdown(f"""
<div class="metric-card">
<div class="metric-label">📅 平均年龄</div>
<div class="metric-value">{avg_age:.1f}</div>
<div style="color: #999; font-size: 0.8rem;">岁</div>
</div>
""", unsafe_allow_html=True)
with col4:
avg_bmi = filtered_df['bmi'].mean() if len(filtered_df) > 0 else 0
st.markdown(f"""
<div class="metric-card">
<div class="metric-label">⚖️ 平均BMI</div>
<div class="metric-value">{avg_bmi:.1f}</div>
<div style="color: #999; font-size: 0.8rem;">{get_bmi_status(avg_bmi)}</div>
</div>
""", unsafe_allow_html=True)
def get_bmi_status(bmi: float) -> str:
"""获取BMI状态"""
if bmi < 18.5:
return "体重过低"
elif bmi < 25:
return "体重正常"
elif bmi < 30:
return "超重"
return "肥胖"
def plot_age_distribution(df: pd.DataFrame):
"""年龄分布图 - 美化版"""
fig = px.histogram(
df,
x='age_years',
color='cardio',
nbins=30,
title="年龄分布趋势",
labels={'age_years': '年龄', 'count': '人数'},
color_discrete_map={0: '#2ecc71', 1: '#e74c3c'},
barmode='overlay',
opacity=0.8
)
fig.update_layout(
template='plotly_white',
title_font_size=18,
title_x=0.5,
legend_title_text="",
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
font=dict(family="Arial, sans-serif", size=12),
margin=dict(t=60, b=40, l=40, r=40)
)
fig.for_each_trace(lambda t: t.update(
name="✅ 健康" if t.name == "0" else "⚠️ 有风险",
marker_line_width=1,
marker_line_color='white'
))
return fig
def plot_bmi_distribution(df: pd.DataFrame):
"""BMI分布饼图"""
bmi_counts = df['bmi_category'].value_counts().reindex(['体重过低', '体重正常', '超重', '肥胖'])
fig = go.Figure(data=[go.Pie(
labels=bmi_counts.index,
values=bmi_counts.values,
hole=0.5,
marker=dict(
colors=COLORS['bmi'],
line=dict(color='white', width=2)
),
textinfo='label+percent',
textposition='outside',
textfont=dict(size=12)
)])
fig.update_layout(
title=dict(text="BMI分布", font=dict(size=18), x=0.5),
template='plotly_white',
showlegend=False,
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
margin=dict(t=60, b=40, l=40, r=40),
annotations=[dict(text='BMI', x=0.5, y=0.5, font_size=20, showarrow=False)]
)
return fig
def plot_bmi_cardio_relation(df: pd.DataFrame):
"""BMI与心血管疾病关系 - 堆叠柱状图"""
bmi_cardio = df.groupby(['bmi_category', 'cardio']).size().unstack(fill_value=0)
bmi_order = ['体重过低', '体重正常', '超重', '肥胖']
bmi_cardio = bmi_cardio.reindex(bmi_order)
fig = go.Figure()
fig.add_trace(go.Bar(
name='✅ 健康',
x=bmi_cardio.index,
y=bmi_cardio[0],
marker_color='#2ecc71',
marker_line=dict(color='white', width=1)
))
fig.add_trace(go.Bar(
name='⚠️ 有风险',
x=bmi_cardio.index,
y=bmi_cardio[1],
marker_color='#e74c3c',
marker_line=dict(color='white', width=1)
))
fig.update_layout(
title=dict(text="BMI与心血管疾病关联分析", font=dict(size=18), x=0.5),
xaxis_title="BMI类别",
yaxis_title="人数",
barmode='stack',
template='plotly_white',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
margin=dict(t=60, b=40, l=40, r=40)
)
return fig
def plot_blood_pressure_scatter(df: pd.DataFrame):
"""血压散点图"""
sample_df = df.sample(min(2000, len(df))) # 采样避免过多点
fig = px.scatter(
sample_df,
x='ap_lo',
y='ap_hi',
color='cardio',
color_discrete_map={0: '#2ecc71', 1: '#e74c3c'},
opacity=0.6,
title="血压分布散点图",
labels={'ap_lo': '舒张压 (mmHg)', 'ap_hi': '收缩压 (mmHg)'}
)
fig.update_layout(
template='plotly_white',
title_font_size=18,
title_x=0.5,
legend_title_text="",
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
margin=dict(t=60, b=40, l=40, r=40)
)
fig.for_each_trace(lambda t: t.update(
name="✅ 健康" if t.name == "0" else "⚠️ 有风险"
))
return fig
def plot_cholesterol_analysis(df: pd.DataFrame):
"""胆固醇与心血管疾病关系"""
chol_cardio = df.groupby(['cholesterol_cat', 'cardio']).size().unstack(fill_value=0)
chol_order = ['正常', '偏高', '非常高']
chol_cardio = chol_cardio.reindex(chol_order)
fig = go.Figure()
fig.add_trace(go.Bar(
name='✅ 健康',
x=chol_cardio.index,
y=chol_cardio[0] if 0 in chol_cardio.columns else [0, 0, 0],
marker_color='#2ecc71'
))
fig.add_trace(go.Bar(
name='⚠️ 有风险',
x=chol_cardio.index,
y=chol_cardio[1] if 1 in chol_cardio.columns else [0, 0, 0],
marker_color='#e74c3c'
))
fig.update_layout(
title=dict(text="胆固醇水平与心血管疾病", font=dict(size=18), x=0.5),
xaxis_title="胆固醇水平",
yaxis_title="人数",
barmode='group',
template='plotly_white',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
margin=dict(t=60, b=40, l=40, r=40)
)
return fig
def render_data_table(df: pd.DataFrame):
"""渲染数据表格"""
st.markdown('<div class="section-title">📋 数据明细</div>', unsafe_allow_html=True)
display_cols = ['id', 'age_years', 'gender', 'height', 'weight', 'bmi', 'bmi_category',
'ap_hi', 'ap_lo', 'cholesterol_cat', 'gluc_cat', 'cardio']
display_df = df[display_cols].copy()
display_df['gender'] = display_df['gender'].map({1: '女性', 2: '男性'})
display_df['cardio'] = display_df['cardio'].map({0: '✅ 健康', 1: '⚠️ 有风险'})
display_df.columns = ['ID', '年龄', '性别', '身高(cm)', '体重(kg)', 'BMI', 'BMI分类',
'收缩压', '舒张压', '胆固醇', '血糖', '心血管状态']
st.dataframe(
display_df,
use_container_width=True,
height=400,
column_config={
"ID": st.column_config.NumberColumn(width="small"),
"年龄": st.column_config.NumberColumn(width="small"),
"心血管状态": st.column_config.TextColumn(width="medium")
}
)
def main():
"""主函数"""
render_header()
# 加载数据
with st.spinner("正在加载数据..."):
raw_df = load_data(DATA_PATH)
if raw_df.empty:
st.error("❌ 数据加载失败，请检查数据文件路径！")
return
# 数据处理
with st.spinner("正在进行数据清洗..."):
df = clean_and_engineer_features(raw_df)
total_count = len(df)
# 侧边栏筛选
filters = create_sidebar(df)
# 应用筛选
filtered_df = apply_filters(df, filters)
# 指标卡片
render_metrics(filtered_df, total_count)
st.markdown("<br>", unsafe_allow_html=True)
# 图表区域
st.markdown('<div class="section-title">📈 可视化分析</div>', unsafe_allow_html=True)
# 第一行图表
col1, col2 = st.columns(2)
with col1:
fig1 = plot_age_distribution(filtered_df)
st.plotly_chart(fig1, use_container_width=True, key="age_chart")
with col2:
fig2 = plot_bmi_distribution(filtered_df)
st.plotly_chart(fig2, use_container_width=True, key="bmi_pie")
# 第二行图表
col3, col4 = st.columns(2)
with col3:
fig3 = plot_bmi_cardio_relation(filtered_df)
st.plotly_chart(fig3, use_container_width=True, key="bmi_cardio")
with col4:
fig4 = plot_cholesterol_analysis(filtered_df)
st.plotly_chart(fig4, use_container_width=True, key="chol_chart")
# 第三行图表
col5, _ = st.columns([1, 1])
with col5:
fig5 = plot_blood_pressure_scatter(filtered_df)
st.plotly_chart(fig5, use_container_width=True, key="bp_scatter")
st.markdown("<br>", unsafe_allow_html=True)
# 数据表格
render_data_table(filtered_df)
# 页脚
st.markdown("<hr>", unsafe_allow_html=True)
st.markdown(
"<p style='text-align: center; color: #999; font-size: 0.9rem;'>❤️ CardioAI © 2024 | 心血管疾病智能分析系统</p>",
unsafe_allow_html=True
)
if __name__ == "__main__":
main()

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"""
CardioAI 模块2: Flask API服务
心血管疾病风险预测 - 后端接口
"""
import joblib
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify, render_template
from pathlib import Path
# ==================== 常量定义 ====================
CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
MODEL_PATH = CODE_ROOT / "module2_predictor" / "cardio_predictor_model.pkl"
# ==================== Flask应用 ====================
app = Flask(__name__,
template_folder='templates',
static_folder='static')
# 全局变量存储模型
model = None
def load_model():
"""加载模型"""
global model
if model is None:
print("📂 正在加载模型...")
model = joblib.load(MODEL_PATH)
print("✅ 模型加载成功!")
return model
# ==================== 路由定义 ====================
@app.route('/')
def index():
"""渲染前端页面"""
return render_template('index.html')
@app.route('/predict_cardio', methods=['POST'])
def predict_cardio():
"""
心血管疾病风险预测接口
接收11个原始特征值的JSON POST请求
返回预测概率和结果
"""
try:
# 获取JSON数据
data = request.get_json()
if not data:
return jsonify({
'success': False,
'error': '未收到数据'
}), 400
# 定义特征列顺序（与训练时一致）
feature_names = [
'age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
'cholesterol', 'gluc', 'smoke', 'alco', 'active'
]
# 从请求中提取特征值
features = []
missing_fields = []
for col in feature_names:
if col in data:
features.append(float(data[col]))
else:
missing_fields.append(col)
features.append(0.0) # 默认值
# 计算BMI: weight / (height/100)^2
weight = float(data.get('weight', 0))
height = float(data.get('height', 0))
if height > 0:
bmi = weight / ((height / 100) ** 2)
features.append(bmi)
else:
features.append(0.0)
if missing_fields:
return jsonify({
'success': False,
'error': f'缺少必要字段: {", ".join(missing_fields)}'
}), 400
# 定义特征列名（与训练时一致）
feature_columns = [
'age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi'
]
# 转换为DataFrame格式
X_input = pd.DataFrame([features], columns=feature_columns)
# 加载模型（如果尚未加载）
predictor = load_model()
# 预测
prediction = int(predictor.predict(X_input)[0])
prob_risk = float(predictor.predict_proba(X_input)[0][1])
prob_healthy = float(predictor.predict_proba(X_input)[0][0])
# 构建响应
result = {
'success': True,
'prediction': prediction,
'prediction_label': '有风险' if prediction == 1 else '健康',
'probability': {
'健康': round(prob_healthy * 100, 2),
'有风险': round(prob_risk * 100, 2)
},
'risk_level': get_risk_level(prob_risk),
'recommendation': get_recommendation(prob_risk, data)
}
return jsonify(result)
except ValueError as e:
return jsonify({
'success': False,
'error': f'数据格式错误: {str(e)}'
}), 400
except Exception as e:
return jsonify({
'success': False,
'error': f'预测失败: {str(e)}'
}), 500
def get_risk_level(probability: float) -> str:
"""根据概率返回风险等级"""
if probability < 0.3:
return '🟢 低风险'
elif probability < 0.5:
return '🟡 中低风险'
elif probability < 0.7:
return '🟠 中高风险'
else:
return '🔴 高风险'
def get_recommendation(probability: float, data: dict) -> str:
"""根据预测结果给出建议"""
if probability < 0.3:
return '继续保持健康的生活方式，定期体检。'
elif probability < 0.5:
return '建议适当增加运动，注意饮食均衡。'
elif probability < 0.7:
return '建议咨询医生，制定健康管理计划。'
else:
return '⚠️ 风险较高，请尽快就医检查。'
@app.route('/health', methods=['GET'])
def health_check():
"""健康检查接口"""
return jsonify({
'status': 'healthy',
'service': 'CardioAI Cardiovascular Prediction API',
'version': '1.0.0'
})
# ==================== 启动应用 ====================
if __name__ == '__main__':
print("\n" + "="*60)
print("❤️ CardioAI 心血管疾病风险预测 API")
print("="*60)
print(f"📂 模型路径: {MODEL_PATH}")
print(f"🌐 启动地址: http://localhost:5001")
print("="*60 + "\n")
# 预加载模型
load_model()
# 启动Flask应用
app.run(
host='0.0.0.0',
port=5001,
debug=True
)

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"""
CardioAI 模块2: 模型训练脚本
心血管疾病风险预测模型 - 训练与保存
"""
import pandas as pd
import numpy as np
import joblib
from pathlib import Path
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_auc_score, accuracy_score
from xgboost import XGBClassifier
# ==================== 常量定义 ====================
CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
DATA_PATH = CODE_ROOT / "data" / "心血管疾病.xlsx"
MODEL_PATH = CODE_ROOT / "module2_predictor" / "cardio_predictor_model.pkl"
# 特征列定义
CONTINUOUS_FEATURES = ['age', 'height', 'weight', 'ap_hi', 'ap_lo', 'bmi']
CATEGORICAL_FEATURES = ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active']
def load_and_clean_data(file_path: Path) -> pd.DataFrame:
"""加载并清洗数据"""
print(f"📂 正在加载数据: {file_path}")
# 加载Excel数据
df = pd.read_excel(file_path, engine='openpyxl')
print(f"✅ 数据加载成功，共 {len(df)} 条记录")
# 复制数据
df = df.copy()
# 特征工程: age(天) -> age_years
df['age_years'] = (df['age'] / 365).round().astype(int)
# 计算BMI
df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
# 异常值处理: 删除舒张压 >= 收缩压的记录
initial_count = len(df)
df = df[df['ap_lo'] < df['ap_hi']]
print(f"🗑️ 删除舒张压>=收缩压的记录: {initial_count - len(df)} 条")
# 删除血压极端异常值
# 收缩压 ∈ [90, 250]
initial_count = len(df)
df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
removed_hy = initial_count - len(df)
# 舒张压 ∈ [60, 150]
initial_count = len(df)
df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
removed_lo = initial_count - len(df)
print(f"🗑️ 删除血压异常值: 收缩压 {removed_hy} 条, 舒张压 {removed_lo} 条")
print(f"✅ 数据清洗完成，剩余 {len(df)} 条记录")
return df
def prepare_features(df: pd.DataFrame) -> tuple:
"""
准备特征和标签
删除id和原始age字段，保留处理后的特征
"""
# 定义要使用的特征（删除id和原始age，保留age_years）
feature_columns = ['age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi']
X = df[feature_columns].copy()
y = df['cardio'].copy()
print(f"📊 特征数量: {len(feature_columns)}")
print(f"📊 特征列: {feature_columns}")
return X, y, feature_columns
def build_pipeline() -> Pipeline:
"""构建包含预处理器和分类器的Pipeline"""
print("🔧 正在构建Pipeline...")
# 连续特征列
continuous_cols = ['age_years', 'height', 'weight', 'ap_hi', 'ap_lo', 'bmi']
# 分类特征列
categorical_cols = ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active']
# 预处理器
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), continuous_cols),
('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), categorical_cols)
],
remainder='drop'
)
# 完整Pipeline: 预处理 + XGBoost分类器
pipeline = Pipeline([
('preprocessor', preprocessor),
('classifier', XGBClassifier(
n_estimators=100,
max_depth=6,
learning_rate=0.1,
random_state=42,
use_label_encoder=False,
eval_metric='logloss',
n_jobs=-1
))
])
print("✅ Pipeline构建完成")
return pipeline
def train_and_evaluate(X: pd.DataFrame, y: pd.Series, pipeline: Pipeline):
"""训练模型并评估"""
print("\n" + "="*50)
print("📈 开始模型训练...")
print("="*50)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
print(f"📊 训练集大小: {len(X_train)}")
print(f"📊 测试集大小: {len(X_test)}")
# 训练模型
print("🏋️ 正在训练XGBoost模型...")
pipeline.fit(X_train, y_train)
# 预测
y_pred = pipeline.predict(X_test)
y_pred_proba = pipeline.predict_proba(X_test)[:, 1]
# 评估指标
accuracy = accuracy_score(y_test, y_pred)
roc_auc = roc_auc_score(y_test, y_pred_proba)
print("\n" + "="*50)
print("📊 模型评估结果:")
print("="*50)
print(f"✅ 准确率 (Accuracy): {accuracy:.4f}")
print(f"✅ ROC-AUC 分数: {roc_auc:.4f}")
print("\n📋 分类报告:")
print(classification_report(y_test, y_pred, target_names=['健康', '有风险']))
return pipeline
def save_model(pipeline: Pipeline, model_path: Path):
"""保存模型"""
print(f"\n💾 正在保存模型到: {model_path}")
joblib.dump(pipeline, model_path)
print(f"✅ 模型保存成功!")
# 验证模型文件
file_size = model_path.stat().st_size / (1024 * 1024)
print(f"📦 模型文件大小: {file_size:.2f} MB")
def main():
"""主函数"""
print("\n" + "="*60)
print("❤️ CardioAI 模块2: 心血管疾病风险预测模型训练")
print("="*60 + "\n")
# 1. 加载并清洗数据
df = load_and_clean_data(DATA_PATH)
# 2. 准备特征
X, y, feature_columns = prepare_features(df)
# 3. 构建Pipeline
pipeline = build_pipeline()
# 4. 训练并评估模型
trained_pipeline = train_and_evaluate(X, y, pipeline)
# 5. 保存模型
save_model(trained_pipeline, MODEL_PATH)
print("\n" + "="*60)
print("🎉 模型训练完成!")
print("="*60)
print(f"\n📌 模型使用说明:")
print(f" 1. 启动Flask API: python app.py")
print(f" 2. 访问 http://localhost:5001 查看预测界面")
print(f" 3. 输入11个特征值进行预测")
if __name__ == "__main__":
main()

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# CardioAI 项目依赖
# 创建并激活 conda 环境:
# conda create -n cardioenv python=3.10
# conda activate cardioenv
# 然后安装依赖:
# pip install -r requirements.txt
pandas
openpyxl
numpy
scikit-learn
xgboost
joblib
streamlit
plotly
Flask
python-dotenv
langchain-openai
dashscope
requests