医疗中风数据集
该数据集是一个包含患者人口统计信息、健康状况和生活方式等临床表格数据的医疗数据集，主要用于中风风险预测和医疗数据分析研究，通过分析高血压、心脏病、血糖水平、吸烟状况等多种因素，帮助识别中风相关风险并支持疾病预防和公共卫生决策。
基本信息
资源简介
医疗中风数据集
【数据集背景】
本数据集源站标题为《Pratiksha012/Graph-Analytics-and-Algorithms-on-Stroke-Dataset》。源站首次发布：2024-07-15。
【数据内容】
源站原始描述：This project explores the application of graph analytics and algorithms on a stroke dataset. We conduct a comprehensive case study involving data preprocessing, EDA, graph centrality measures, and various machine learning models. The primary aim is to gain insights from the dataset and improve predictive modelling using graph-based approaches. # Graph Analytics and Algorithms on Healthcare Stroke Dataset ## Introduction This project explores the application of graph analytics and algorithms on a…
【获取与许可】
源站页面：https://github.com/Pratiksha012/Graph-Analytics-and-Algorithms-on-Stroke-Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/Pratiksha012/Graph-Analytics-and-Algorithms-on-Stroke-Dataset.git
curl -L -o repo.zip https://github.com/Pratiksha012/Graph-Analytics-and-Algorithms-on-Stroke-Dataset/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Graph Analytics and Algorithms on Healthcare Stroke Dataset
Introduction
This project explores the application of graph analytics and algorithms on a healthcare stroke dataset. We conduct a comprehensive case study involving data preprocessing, exploratory data analysis (EDA), graph centrality measures, and various machine learning models. The primary aim is to gain insights from the dataset and improve predictive modeling using graph-based approaches.
Dataset
The dataset used in this study is focused on healthcare data related to strokes. It contains the following columns:
- id: Unique identifier for each patient.
- gender: Gender of the patient.
- age: Age of the patient.
- hypertension: Whether the patient has hypertension (1) or not (0).
- heart_disease: Whether the patient has heart disease (1) or not (0).
- ever_married: Marital status of the patient.
- work_type: Type of work the patient is involved in.
- Residence_type: Type of residence (Urban/Rural).
- avg_glucose_level: Average glucose level in the blood.
- bmi: Body Mass Index of the patient.
- smoking_status: Smoking status of the patient.
- stroke: Whether the patient has experienced a stroke (1) or not (0).
Project Structure
1. Data Preprocessing and Cleaning
- Data Cleaning: Handled missing values, outliers, and irrelevant data to ensure the dataset is suitable for analysis.
- Label Encoding: Converted categorical data into numerical labels for analysis, making it easier to apply machine learning algorithms.
- Exploratory Data Analysis (EDA): Conducted EDA to understand the distribution of various features, identify patterns, and gain initial insights into the data. This included visualizations and statistical analysis.
2. Graph Centrality Analysis
- Graph Construction: Created graphs representing relationships within the data. Each node represents a patient, and edges represent relationships based on various features.
- Centrality Measures:
- Degree Centrality: Identified nodes (patients) with the highest number of connections, indicating potential key influencers.
- Closeness Centrality: Measured the average shortest path from a node to all other nodes, indicating the speed of information spread within the network.
- Betweenness Centrality: Determined nodes that act as bridges between ot
完整仓库：github.com/Pratiksha012/Graph-Analytics-and-Algorithms-on-Stroke-Dataset
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
