各种机器学习模型的性能指标数据集
包含南非结核病诊断中多种ML模型（RF、DT、SVM等）的性能指标数据集。
基本信息
资源简介
各种机器学习模型的性能指标数据集
【数据集背景】
本数据集源站标题为《The performance metrics for various ML models.》。源站首次发布：2025-11-07。
【数据内容】
源站原始描述：The Ministry of Health and Social Welfare of South Africa has made significant efforts to combat tuberculosis (TB), guided by the National Strategic Plan for addressing HIV, STIs, and TB. However, progress in preventing and eradicating TB has been seriously hindered by reliance on ineffective diagnostic methods. This study aimed to predict and improve TB diagnosis in South Africa using machine learning techniques. Data from the National Income Dynamics Survey, conducted by the Southern African L…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 3.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pgph.0004674.t003。
【主题与分类】
源站学科分类：Microbiology、Biotechnology、Biological Sciences not elsewhere classified、Science Policy、Infectious Diseases。主题标签：southern african labour、sgbms showed 91、rank variable importance、predictive technique may、national strategic plan、ineffective diagnostic methods、development research units、ann yielded 81、made significant efforts、enhancing tb prediction。
【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/The_performance_metrics_for_various_ML_models_/30568736
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Asori, Moses; Mpuure, Desmond Mbe-Nyire; Katey, Daniel; Gyasi, Razak M. (2025). The performance metrics for various ML models.. PLOS Global Public Health. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：The performance metrics for various ML models.
- DOI：10.1371/journal.pgph.0004674.t003
- 发布日期：2025-11-07
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/The_performance_metrics_for_various_ML_models_/30568736
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/59400551"
文件清单
Table 3.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
The Ministry of Health and Social Welfare of South Africa has made significant efforts to combat tuberculosis (TB), guided by the National Strategic Plan for addressing HIV, STIs, and TB. However, progress in preventing and eradicating TB has been seriously hindered by reliance on ineffective diagnostic methods. This study aimed to predict and improve TB diagnosis in South Africa using machine learning techniques. Data from the National Income Dynamics Survey, conducted by the Southern African Labour and Development Research Units, were analyzed. The dataset underwent a 70:30 train-test split for Random Forest (RF), Decision Trees (DTs), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), Artificial Neural Networks (ANNs), and Logistic Regression (LR). Hyperparameter tuning and impurity-based measures were employed to rank variable importance. RF achieved 87.50% sensitivity and an F1-score of 92.5%. DT achieved a sensitivity of 90.92% and an F1-score of 93.01%. ANN yielded 81.72% sensitivity and an F1-score of 87.53%. SGBMs showed 91.32% sensitivity and 94.55% F1-score. SVMs showed 90.03% sensitivity and 97.72% F1-score. LR achieved a sensitivity of 96.55% and an F1-score of 96.80%. Machine Learning (ML) techniques, with accuracy rates of more than 80% present a significant opportunity for enhancing TB prediction and diagnosis in South Africa. This predictive technique may be beneficial in resource-constrained settings, including those in sub-Saharan Africa.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pgph.0004674.t003（source: https://plos.figshare.com/articles/dataset/The_performance_metrics_for_various_ML_models_/30568736）。
数据缺失？
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