与其他作者的准确性比较
包含乳腺癌诊断的原始及合成数据集，比较多种机器学习模型的准确性。
基本信息
资源简介
与其他作者的准确性比较
【数据集背景】
本数据集源站标题为《Comparison of accuracies with other authors.》。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 5.63 KB。Table 8.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0326221.t008。
【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】
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://plos.figshare.com/articles/dataset/Comparison_of_accuracies_with_other_authors_/29359266
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). Comparison of accuracies with other authors.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Comparison of accuracies with other authors.
- DOI：10.1371/journal.pone.0326221.t008
- 发布日期：2025-06-18
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Comparison_of_accuracies_with_other_authors_/29359266
命令行下载
curl -L -o "Table 8.xls" "https://ndownloader.figshare.com/files/55478388"
文件清单
Table 8.xls（5.63 KB）
文件总体积 5.63 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.t008（source: https://plos.figshare.com/articles/dataset/Comparison_of_accuracies_with_other_authors_/29359266）。
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