乳腺癌数据集
该数据集是一个包含32列特征的医学表格数据,涵盖肿瘤的恶性或良性诊断标签以及形态学特征,主要用于乳腺癌诊断相关的机器学习模型训练、分类预测及医学数据分析研究。

基本信息
资源简介
乳腺癌数据集
【数据集背景】
本数据集源站标题为《Way4ward17/ML_Breast_Cancer_Dataset_Using10KFolds》。源站首次发布:2020-01-13。
【数据内容】
源站原始描述:This is a machine learning project. Using 10KFold Cross Validation. Breast cancer the most common cancer among women worldwide accounting for 25 percent of all cancer cases and affected 2.1 million people in 2015 early diagnosis significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can d # ML_Breast_Cancer_Dataset_Using10KFolds CHAPTER THREE RESEARCH METHODOLOGY 3.1 INTRODUCTION Breast…
【获取与许可】
源站页面:https://github.com/Way4ward17/ML_Breast_Cancer_Dataset_Using10KFolds
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/Way4ward17/ML_Breast_Cancer_Dataset_Using10KFolds.git
curl -L -o repo.zip https://github.com/Way4ward17/ML_Breast_Cancer_Dataset_Using10KFolds/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录(使用方式)
ML_Breast_Cancer_Dataset_Using10KFolds
<p><strong>CHAPTER THREE</strong></p>
<p><strong>RESEARCH METHODOLOGY</strong></p>
<p><strong>3.1 INTRODUCTION</strong></p>
<p>Breast cancer the most common cancer among women worldwide accounting for 25 percent of all cancer cases and affected 2.1 million people in 2015 early diagnosis significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis. Research indicates that most experienced physicians can diagnose cancer with 79 percent accuracy while 91 percent correct diagnosis is achieved using machine learning techniques.</p>
<p>In this case study, our task is to classify tumors into malignant or benign tumors using features of pain from several cell image.</p>
<p><strong>3.2 METHODOLOGY</strong></p>
<p>Methodology is the systematic, theoretical analysis of the methods applied to a field of study. It comprises the theoretical analysis of the body of methods and principles associated with a branch of knowledge. Typically, it encompasses concepts such as paradigm, theoretical model, phases and quantitative or qualitative techniques. A methodology does not set out to provide solutions it is therefore, not the same as a method. Instead, a methodology offers the theoretical underpinning for understanding which method, set of methods, or best practices can be applied to a specific case, for example, to calculate a specific result.</p>
<p>In other words, methodology is the general research strategy that outlines the way in which research is to be undertaken and, among other things, identifies the methods to be used in it. These methods, described in the methodology, define the means or modes of data collection or, sometimes, how a specific result is to be calculated. Methodology does not define specific methods, even though much attention is given to the nature and kinds of processes to be followed in a particular procedure or to attain an objective. When proper to a study of methodology, such processes constitute a constructive generic framework, and may therefore be broken down into sub-processes, combined, or their sequence changed. (Katsicas, 2009)</p>
<p>In this work, we have considered the use
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))
完整仓库:github.com/Way4ward17/ML_Breast_Cancer_Dataset_Using10KFolds
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。




