脑卒中分析数据集
该数据集包含脑卒中相关的临床数据，数据模态主要为表格形式的医疗记录，通过应用机器学习模型和SMOTEENN等重采样技术处理数据不平衡问题，主要用于脑卒中的风险预测、早期诊断以及医疗人工智能模型的开发与研究。
基本信息
资源简介
该数据集是一个用于脑卒中（中风）分析的医学数据集，主要包含与脑卒中相关的临床或医疗数据。数据集通过应用机器学习模型（如分类算法）和重采样技术（如SMOTEENN）来处理数据不平衡问题，旨在提高脑卒中预测的准确性。数据来源可能包括临床记录、医疗影像或公开的医学数据库。该数据集适用于脑卒中的风险预测、早期诊断以及医疗人工智能模型的研究与开发。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis.git
curl -L -o repo.zip https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Cereberal-Stroke-Analysis
Followed Process
Read Data:
The script starts by importing necessary libraries (pandas, numpy, seaborn, matplotlib.pyplot) and reading a CSV file into a DataFrame (df).
Exploratory Data Analysis (EDA):
Basic exploration of the dataset using head(), describe(), and checking for missing values using isnull().sum().
Handling Categorical Variables:
One-hot encoding is performed on categorical variables using pd.get_dummies().
Handling Missing Values:
Missing values are imputed using the k-nearest neighbors algorithm (KNNImputer from sklearn.impute).
Feature Scaling and Train-Test Split:
Features are scaled using MinMaxScaler, and the dataset is split into training and testing sets.
Model Selection:
Several classification models are chosen (KNeighborsClassifier, GaussianNB, DecisionTreeClassifier, and RandomForestClassifier) for initial testing.
Model Evaluation Without Resampling:
Classification reports are generated for each model to evaluate their performance on the imbalanced dataset.
<p align=“center”>
<img src=“https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis/blob/main/Images/Before Sampling.png”>
</p>
OverSampling (SMOTE):
The script uses the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority class.
Model Evaluation After OverSampling:
The same models are re-trained and evaluated on the oversampled dataset.
<p align=“center”>
<img src=“https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis/blob/main/Images/OverSampling.png”>
</p>
UnderSampling:
Random under-sampling is performed to balance the class distribution.
Model Evaluation After UnderSampling:
The models are re-trained and evaluated on the undersampled dataset.
<p align=“center”>
<img src=“https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis/blob/main/Images/UnderSampling.png”>
</p>
Combining OverSampling and UnderSampling (SMOTEENN):
The SMOTEENN technique, which combines SMOTE and Edited Nearest Neighbours (ENN), is applied.
Model Evaluation After Combining OverSampling and UnderSampling:
The models are re-trained and evaluated on the combined dataset.
<p align=“center”>
<img src=“https://github.com/Demon-2-Angel/Cereberal-Stroke-Analysis/blob/main/Images/After Over %26 Under Sampling.png”>
</p>
Conclusion:
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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