Zane Xu
3a3394da49
1. 创建module1_dashboard/cardio_dashboard.py - Streamlit应用程序，提供交互式数据可视化 - 包含数据清洗、特征工程、异常值处理 - 集成年龄转换、BMI计算、类别转换 - 侧边栏筛选器、Plotly图表展示 - 智能路径解析，支持多种运行环境 2. 添加data/心血管疾病.xlsx数据文件 - 心血管疾病原始数据集 - 用于模型训练和可视化分析 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
395 lines
12 KiB
Python
395 lines
12 KiB
Python
#!/opt/anaconda3/envs/cardioenv/bin/python
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"""
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CardioAI - 心血管疾病数据可视化仪表板
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Streamlit应用程序，用于对心血管疾病数据进行清洗、特征工程和交互式可视化
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"""
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.express as px
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import warnings
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import os
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warnings.filterwarnings('ignore')
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# 页面配置
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st.set_page_config(
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page_title="CardioAI - 心血管疾病智能分析",
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page_icon="❤️",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# 数据路径
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DATA_PATH = "./data/心血管疾病.xlsx"
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@st.cache_data(ttl=3600)
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def load_and_process_data():
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"""
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加载并处理心血管疾病数据
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返回处理后的DataFrame
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"""
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try:
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# 调试信息：检查文件路径
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current_dir = os.getcwd()
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script_dir = os.path.dirname(os.path.abspath(__file__))
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st.sidebar.info(f"当前工作目录: {current_dir}")
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st.sidebar.info(f"脚本目录: {script_dir}")
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st.sidebar.info(f"数据路径: {DATA_PATH}")
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# 尝试多种路径
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possible_paths = [
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os.path.abspath(DATA_PATH), # 绝对路径
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DATA_PATH, # 原始相对路径
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os.path.join(current_dir, DATA_PATH),
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os.path.join(current_dir, "data", "心血管疾病.xlsx"),
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os.path.join(script_dir, "..", DATA_PATH),
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os.path.join(script_dir, "..", "data", "心血管疾病.xlsx"),
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os.path.join(script_dir, "data", "心血管疾病.xlsx")
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]
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data_path = None
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for path in possible_paths:
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if os.path.exists(path):
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data_path = path
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st.sidebar.success(f"找到数据文件: {path}")
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break
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if data_path is None:
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st.sidebar.error("未找到数据文件，请检查路径")
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st.sidebar.info(f"尝试过的路径: {possible_paths}")
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return pd.DataFrame()
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# 加载数据
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df = pd.read_excel(data_path)
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# 1. 特征工程
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# 将age(天)转换为年，四舍五入
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df['age_years'] = (df['age'] / 365.25).round().astype(int)
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# 计算BMI: weight / (height/100)^2
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df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
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# 2. 异常值处理
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# 删除舒张压 >= 收缩压的记录
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df = df[df['ap_lo'] < df['ap_hi']].copy()
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# 删除血压极端异常值
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# 收缩压 ∈ [90, 250], 舒张压 ∈ [60, 150]
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df = df[
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(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250) &
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(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)
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].copy()
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# 3. 类别转换
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# cholesterol转换
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cholesterol_map = {
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1: '正常',
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2: '高于正常',
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3: '很高'
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}
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df['cholesterol_str'] = df['cholesterol'].map(cholesterol_map)
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# gluc转换
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gluc_map = {
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1: '正常',
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2: '高于正常',
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3: '很高'
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}
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df['gluc_str'] = df['gluc'].map(gluc_map)
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# BMI分类
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def categorize_bmi(bmi):
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if bmi < 18.5:
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return '偏瘦'
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elif 18.5 <= bmi < 25:
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return '正常'
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elif 25 <= bmi < 30:
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return '超重'
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else:
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return '肥胖'
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df['bmi_category'] = df['bmi'].apply(categorize_bmi)
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# 添加血压分类
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df['bp_category'] = pd.cut(
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df['ap_hi'],
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bins=[0, 120, 140, 160, 180, 250],
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labels=['正常', '偏高', '高血压1级', '高血压2级', '高血压3级']
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)
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return df
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except Exception as e:
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st.error(f"数据加载失败: {e}")
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return pd.DataFrame()
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def create_filters(df):
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"""
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创建侧边栏筛选器
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返回筛选后的数据
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"""
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st.sidebar.header("🔍 数据筛选")
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# 年龄范围滑块
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min_age = int(df['age_years'].min())
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max_age = int(df['age_years'].max())
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age_range = st.sidebar.slider(
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"选择年龄范围",
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min_value=min_age,
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max_value=max_age,
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value=(min_age, max_age),
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step=1
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)
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# 性别筛选（多选）
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gender_options = df['gender'].unique()
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gender_options = sorted(gender_options)
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gender_labels = {1: '女性', 2: '男性'}
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selected_genders = st.sidebar.multiselect(
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"选择性别",
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options=gender_options,
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default=gender_options,
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format_func=lambda x: gender_labels.get(x, f"性别{x}")
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)
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# 心血管疾病筛选（多选）
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cardio_options = df['cardio'].unique()
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cardio_options = sorted(cardio_options)
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cardio_labels = {0: '无疾病', 1: '有疾病'}
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selected_cardio = st.sidebar.multiselect(
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"选择心血管疾病状态",
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options=cardio_options,
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default=cardio_options,
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format_func=lambda x: cardio_labels.get(x, f"状态{x}")
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)
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# 应用筛选
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filtered_df = df[
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(df['age_years'] >= age_range[0]) &
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(df['age_years'] <= age_range[1]) &
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(df['gender'].isin(selected_genders)) &
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(df['cardio'].isin(selected_cardio))
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].copy()
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return filtered_df, age_range, selected_genders, selected_cardio
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def display_metrics(filtered_df, original_df):
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"""
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显示关键指标
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"""
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric(
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label="总记录数",
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value=f"{len(filtered_df):,}",
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delta=f"{len(filtered_df) - len(original_df):+,}" if len(filtered_df) != len(original_df) else None
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)
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with col2:
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disease_count = filtered_df['cardio'].sum()
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disease_rate = (disease_count / len(filtered_df) * 100) if len(filtered_df) > 0 else 0
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st.metric(
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label="心血管疾病风险率",
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value=f"{disease_rate:.1f}%",
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delta=f"{disease_count:,} 例"
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)
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with col3:
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avg_age = filtered_df['age_years'].mean()
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st.metric(
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label="平均年龄",
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value=f"{avg_age:.1f} 岁"
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)
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with col4:
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avg_bmi = filtered_df['bmi'].mean()
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st.metric(
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label="平均BMI",
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value=f"{avg_bmi:.1f}"
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)
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def create_visualizations(filtered_df):
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"""
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创建可视化图表
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"""
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st.subheader("📊 数据分析")
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# 创建两列布局
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("##### 年龄分布（按心血管疾病状态）")
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if not filtered_df.empty:
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fig1 = px.histogram(
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filtered_df,
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x='age_years',
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color='cardio',
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nbins=30,
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barmode='overlay',
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color_discrete_map={0: '#636EFA', 1: '#EF553B'},
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labels={
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'age_years': '年龄（岁）',
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'cardio': '心血管疾病',
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'count': '人数'
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},
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category_orders={'cardio': [0, 1]},
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opacity=0.7
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)
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fig1.update_layout(
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legend_title_text='疾病状态',
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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)
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)
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st.plotly_chart(fig1, use_container_width=True)
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else:
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st.info("没有数据可显示")
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with col2:
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st.markdown("##### BMI分类与心血管疾病关系")
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if not filtered_df.empty:
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# 创建交叉表
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bmi_cardio_cross = pd.crosstab(
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filtered_df['bmi_category'],
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filtered_df['cardio'],
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normalize='index'
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).reset_index()
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# 转换为长格式
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bmi_cardio_long = pd.melt(
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bmi_cardio_cross,
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id_vars=['bmi_category'],
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value_vars=[0, 1],
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var_name='cardio',
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value_name='proportion'
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)
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# 添加标签
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bmi_cardio_long['cardio_label'] = bmi_cardio_long['cardio'].map({0: '无疾病', 1: '有疾病'})
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fig2 = px.bar(
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bmi_cardio_long,
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x='bmi_category',
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y='proportion',
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color='cardio_label',
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barmode='stack',
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color_discrete_map={'无疾病': '#00CC96', '有疾病': '#AB63FA'},
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labels={
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'bmi_category': 'BMI分类',
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'proportion': '比例',
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'cardio_label': '疾病状态'
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},
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category_orders={
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'bmi_category': ['偏瘦', '正常', '超重', '肥胖']
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}
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)
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fig2.update_layout(
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yaxis_tickformat=',.0%',
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legend_title_text='疾病状态'
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)
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st.plotly_chart(fig2, use_container_width=True)
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else:
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st.info("没有数据可显示")
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# 额外分析
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st.subheader("🔍 详细分析")
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col3, col4 = st.columns(2)
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with col3:
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st.markdown("##### 胆固醇水平分布")
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if not filtered_df.empty:
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fig3 = px.pie(
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filtered_df,
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names='cholesterol_str',
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color='cholesterol_str',
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color_discrete_sequence=px.colors.sequential.RdBu
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)
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fig3.update_traces(textposition='inside', textinfo='percent+label')
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st.plotly_chart(fig3, use_container_width=True)
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with col4:
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st.markdown("##### 血糖水平分布")
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if not filtered_df.empty:
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fig4 = px.pie(
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filtered_df,
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names='gluc_str',
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color='gluc_str',
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color_discrete_sequence=px.colors.sequential.Bluyl
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)
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fig4.update_traces(textposition='inside', textinfo='percent+label')
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st.plotly_chart(fig4, use_container_width=True)
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def display_data_summary(filtered_df):
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"""
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显示数据摘要
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"""
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with st.expander("📋 数据摘要（点击展开）"):
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st.dataframe(
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filtered_df.describe().round(2),
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use_container_width=True
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)
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st.markdown("##### 数据样本")
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st.dataframe(
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filtered_df.head(10),
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use_container_width=True
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)
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def main():
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"""
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主函数
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"""
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# 标题和介绍
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st.title("❤️ CardioAI - 心血管疾病智能分析系统")
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st.markdown("""
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本仪表板提供心血管疾病数据的交互式可视化分析。使用侧边栏筛选器探索数据模式。
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""")
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# 加载数据
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with st.spinner('正在加载和处理数据...'):
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df = load_and_process_data()
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if df.empty:
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st.error("无法加载数据。请检查数据文件路径和格式。")
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return
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# 创建筛选器并获取筛选后的数据
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filtered_df, age_range, selected_genders, selected_cardio = create_filters(df)
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# 显示筛选条件
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st.sidebar.markdown("---")
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st.sidebar.markdown(f"**筛选结果:** {len(filtered_df):,} 条记录")
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# 显示关键指标
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display_metrics(filtered_df, df)
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# 显示可视化图表
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create_visualizations(filtered_df)
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# 显示数据摘要
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display_data_summary(filtered_df)
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# 页脚
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st.markdown("---")
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st.caption("CardioAI - 心血管疾病智能辅助系统 | Module 1: 数据可视化仪表板")
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if __name__ == "__main__":
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main()
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