卵巢癌数据集
包含216名患者和4000个遗传标记的卵巢癌数据集，用于分类诊断。
基本信息
资源简介
卵巢癌数据集
【数据集背景】
本数据集源站标题为《PastryDoll/Ovarian-Cancer》。源站首次发布：2022-12-18。
【数据内容】
源站原始描述：We analize the Ovarian Cancer dataset. Using SVM we obtain recall score = 1. # Ovarian-Cancer In this notebook we will analize the Ovarian Cancer Dataset. This is a dataset, that come with MATLAB, consists of a 216x4000 matrix of 216 patients (rows), and 4000 (columns) indicating genetic markers measured on each patient. We also have an array that indicates if the patient have ovarian cancer or not. Our objective is to perform a PCA on this matrix, and extract from it its 3 principal components.…
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://github.com/PastryDoll/Ovarian-Cancer
使用许可：MIT。
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/PastryDoll/Ovarian-Cancer.git
curl -L -o repo.zip https://github.com/PastryDoll/Ovarian-Cancer/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Ovarian-Cancer
In this notebook we will analize the Ovarian Cancer Dataset. This is a dataset, that come with MATLAB, consists of a 216x4000 matrix of 216 patients (rows), and 4000 (columns) indicating genetic markers measured on each patient. We also have an array that indicates if the patient have ovarian cancer or not.
Our objective is to perform a PCA on this matrix, and extract from it its 3 principal components. After performing the PCA, we will plot the transformed data point, and investigate if they seen to divide into clusters, this will tell us if it is natural to ask for a classification model to fit our data.
We obtained a recall score of 1.
Confusion Matrix for Test Set:
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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。
