癌症患者数据集
该数据集是一个包含癌症患者人口统计学信息、环境风险因素暴露情况以及临床症状的临床表格数据，涵盖了患者ID、年龄、性别、吸烟史、职业危害、遗传风险、慢性肺病、胸痛、咳血、疲劳等多种特征，主要用于癌症风险因素分析、疾病预测模型构建以及公共卫生领域的健康数据研究。
基本信息
资源简介
癌症患者数据集
【数据集背景】
本数据集源站标题为《Arjun-08/Dimensionality-Reduction-in-Oncology-》。源站首次发布：2024-05-17。
【数据内容】
源站原始描述：This project involves the application of dimensionality reduction techniques i.e., Principal Component Analysis (PCA), on a cancer patients dataset. The goal is to simplify the dataset by reducing its dimensionality, making it easier to visualize and analyze, while retaining essential information. # Dimensionality-Reduction-in-Oncology ## Overview This project demonstrates the application of dimensionality reduction techniques on a cancer patients dataset to simplify data analysis and uncover hi…
【主题与分类】
主题标签：cancer-dataset、dimensionality-reduction、machine-learning、pca、unsupervised-learning。
【获取与许可】
源站页面：https://github.com/Arjun-08/Dimensionality-Reduction-in-Oncology-
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/Arjun-08/Dimensionality-Reduction-in-Oncology-.git
curl -L -o repo.zip https://github.com/Arjun-08/Dimensionality-Reduction-in-Oncology-/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Dimensionality-Reduction-in-Oncology
Overview
This project demonstrates the application of dimensionality reduction techniques on a cancer patients dataset to simplify data analysis and uncover hidden patterns. By reducing the number of features, we aim to facilitate better visualization and improve the performance of machine learning models.
Dataset
The dataset used in this project contains information on cancer patients, including demographic details, exposure to risk factors, and clinical symptoms. The dataset consists of the following features:
- Patient Id
- Age
- Gender
- Air Pollution
- Alcohol use
- Dust Allergy
- Occupational Hazards
- Genetic Risk
- Chronic Lung Disease
- Balanced Diet
- Obesity
- Smoking
- Passive Smoker
- Chest Pain
- Coughing of Blood
- Fatigue
- Weight Loss
- Shortness of Breath
- Wheezing
- Swallowing Difficulty
- Clubbing of Finger Nails
- Frequent Cold
- Dry Cough
- Snoring
- Level
Project Workflow
Data Preprocessing
- Loading the Dataset: The dataset is loaded from an Excel file stored in Google Drive.
- Handling Missing Values: Rows with missing values (encoded as 0 or ‘‘Nan’’) are removed to ensure data quality.
- Label Encoding: Categorical variables are encoded using label encoding to convert them into numerical values.
- Data Scaling: The data is standardized using the
StandardScalerto normalize the distribution of features.
Dimensionality Reduction
- Principal Component Analysis (PCA): PCA is applied to reduce the dimensionality of the dataset to 2 principal components, retaining the essential information while simplifying the data.
Results
- Explained Variance: The principal components explain a significant portion of the variance in the data.
- Visualization: The reduced data is visualized using a scatter plot, with different colors representing different levels of cancer severity.
- Component Analysis: The contribution of each original feature to the principal components is analyzed and presented.
Correlation Analysis
A heatmap of the correlation matrix is generated to visualize the relationships between different features in the dataset.
Conclusion
This project demonstrates the effectiveness of PCA in reducing the dimensionality of a complex dataset, making it easier to visualize and analyze. The correlation heatmap provides additional ins
完整仓库：github.com/Arjun-08/Dimensionality-Reduction-in-Oncology-
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。
