148 lines
3.8 KiB
Python
148 lines
3.8 KiB
Python
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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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# 数据路径
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DATA_PATH = "D:\\AI_Coding\\data\\心血管疾病.xlsx"
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# 缓存数据加载和清洗函数
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@st.cache_data
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def load_and_preprocess_data():
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# 加载数据
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df = pd.read_excel(DATA_PATH)
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# 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'] = round(df['age'] / 365.25, 0)
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# 计算BMI
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df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
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# 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']]
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# 删除血压极端异常值
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df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
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df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
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# 类别转换
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# 将cholesterol数值转换为描述性字符串
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cholesterol_map = {1: '正常', 2: '偏高', 3: '很高'}
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df['cholesterol_desc'] = df['cholesterol'].map(cholesterol_map)
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# 将gluc数值转换为描述性字符串
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gluc_map = {1: '正常', 2: '偏高', 3: '很高'}
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df['gluc_desc'] = df['gluc'].map(gluc_map)
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# 创建bmi_category
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def get_bmi_category(bmi):
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if bmi < 18.5:
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return '偏瘦'
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elif bmi < 24:
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return '正常'
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elif bmi < 28:
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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(get_bmi_category)
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return df
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# 加载数据
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df = load_and_preprocess_data()
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# Streamlit应用
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st.title('CardioAI - 心血管疾病数据可视化')
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# 侧边栏筛选器
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st.sidebar.header('数据筛选')
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# age_years范围滑块
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age_min = int(df['age_years'].min())
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age_max = int(df['age_years'].max())
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age_range = st.sidebar.slider(
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'年龄范围',
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min_value=age_min,
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max_value=age_max,
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value=(age_min, age_max)
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)
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# gender多选框
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gender_options = df['gender'].unique()
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gender_filter = st.sidebar.multiselect(
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'性别 (1=女, 2=男)',
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options=gender_options,
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default=gender_options
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)
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# cardio多选框
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cardio_options = df['cardio'].unique()
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cardio_filter = st.sidebar.multiselect(
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'心血管疾病 (0=无, 1=有)',
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options=cardio_options,
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default=cardio_options
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)
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# 应用筛选器
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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(gender_filter)) &
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(df['cardio'].isin(cardio_filter))
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]
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# 主页展示
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st.header('数据概览')
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# 展示筛选后的总记录数
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st.metric('筛选后的总记录数', len(filtered_df))
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# 计算并展示心血管疾病总风险率
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if len(filtered_df) > 0:
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cardio_rate = (filtered_df['cardio'].sum() / len(filtered_df)) * 100
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st.metric('心血管疾病总风险率', f"{cardio_rate:.2f}%")
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# 图表
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st.header('数据可视化')
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# age_years分布直方图（按cardio区分）
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st.subheader('年龄分布与心血管疾病关系')
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fig_age = 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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barmode='overlay',
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nbins=20,
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labels={'age_years': '年龄', 'cardio': '心血管疾病', 'count': '人数'},
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title='年龄分布 histogram'
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)
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fig_age.update_layout(
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xaxis_title='年龄',
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yaxis_title='人数',
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legend_title='心血管疾病'
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)
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st.plotly_chart(fig_age)
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# bmi_category对cardio影响的堆叠柱状图
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st.subheader('BMI类别与心血管疾病关系')
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fig_bmi = px.histogram(
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filtered_df,
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x='bmi_category',
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color='cardio',
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barmode='stack',
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labels={'bmi_category': 'BMI类别', 'cardio': '心血管疾病', 'count': '人数'},
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title='BMI类别对心血管疾病影响的堆叠柱状图'
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)
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fig_bmi.update_layout(
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xaxis_title='BMI类别',
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yaxis_title='人数',
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legend_title='心血管疾病'
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)
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st.plotly_chart(fig_bmi)
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# 展示数据样本
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st.header('数据样本')
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st.dataframe(filtered_df.head())
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