文献综述摘要
该数据集包含利用机器学习改进乳腺癌诊断的文献综述摘要，涉及多种算法和合成数据。
基本信息
资源简介
文献综述摘要
【数据集背景】
本数据集源站标题为《Literature review summary.》。源站首次发布：2025-06-18。
【数据内容】
源站原始描述：Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. The objective is to compare the efficiency and accuracy of these models using original and synthetic datasets, contributing to the…
【数据结构与技术规格】
文件数 1 个，合计 13.82 KB。Table 1.xls：application/vnd.ms-excel，13.82 KB。数据集 DOI：10.1371/journal.pone.0326221.t001。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：predicting breast cancer、necessitating improved approaches、improve prediction performance、improve prediction accuracy、breast cancer diagnosis、traditional ml models、models using original、based ensemble strategies、ensemble models、ml models。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Literature_review_summary_/29359242
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Ahmed, Kazi Arman; Humaira, Israt; Khan, Ashiqur Rahman; Hasan, Md Shamim; Islam, Mukitul; Roy, Anik; et al. (2025). Literature review summary.. PLOS ONE. 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（Figshare）
- 源站标题：Literature review summary.
- DOI：10.1371/journal.pone.0326221.t001
- 发布日期：2025-06-18
- 文件数：1 个
- 文件总体积：13.82 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Literature_review_summary_/29359242
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/55478367"
文件清单
Table 1.xls（13.82 KB）
文件总体积 13.82 KB（源站 API 实测）。
数据说明
Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. The objective is to compare the efficiency and accuracy of these models using original and synthetic datasets, contributing to the advancement of breast cancer diagnosis. The methodology comprises three phases, each with two stages. In the first stage of each phase, stratified K-fold cross-validation was performed to train and evaluate multiple ML models. The second stage involved DL-based and AutoML-based ensemble strategies to improve prediction accuracy. In the second and third phases, synthetic data generation methods, such as Gaussian Copula and TVAE, were utilized. The KNN model outperformed others on the original dataset, while the AutoML approach using H2OXGBoost using synthetic data also showed high accuracy. These findings underscore the effectiveness of traditional ML models and AutoML in predicting breast cancer. Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0326221.t001（source: https://plos.figshare.com/articles/dataset/Literature_review_summary_/29359242）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
