丙型肝炎病毒诊断数据集（原始变量描述）
包含615名疑似丙型肝炎感染者人口统计信息的表格数据集，用于集成机器学习诊断模型评估。
基本信息
资源简介
丙型肝炎病毒诊断数据集（原始变量描述）
【数据集背景】
本数据集源站标题为《Dataset variables described (in Raw Form).》。源站首次发布：2025-06-26。
【数据内容】
源站原始描述：Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. The study utilizes a dataset comprising demographic information of 615 individuals suspected of having HCV infection. Additionally, the research employs oversampling and un…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0326488.t001。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Science Policy、Infectious Diseases。主题标签：world health organization、hepatitis c virus、evaluated using metrics、address class imbalances、615 individuals suspected、research employs oversampling、early diagnostic methods、ensemble ml approaches、cv ), area、predict hcv infection。
【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/Dataset_variables_described_in_Raw_Form_/29420039
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tusher, Ekramul Haque; Ismail, Mohd Arfian; Akib, Abdullah; Gabralla, Lubna A.; Ibrahim, Ashraf Osman; Som, Hafizan Mat; et al. (2025). Dataset variables described (in Raw Form).. 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：Dataset variables described (in Raw Form).
- DOI：10.1371/journal.pone.0326488.t001
- 发布日期：2025-06-26
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Dataset_variables_described_in_Raw_Form_/29420039
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/55698008"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. The study utilizes a dataset comprising demographic information of 615 individuals suspected of having HCV infection. Additionally, the research employs oversampling and undersampling techniques to address class imbalances in the dataset and conducts feature reduction using the F-test in one-way analysis of variance. Ensemble ML methods, including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT), are used to predict HCV infection. The performance of these ensemble methods is evaluated using metrics such as accuracy, recall, precision, F1 score, G-mean, balanced accuracy, cross-validation (CV), area under the curve (AUC), standard deviation, and error rate. Compared with previous studies, the Bagging k-NN model demonstrated superior performance under oversampling conditions, achieving 98.37% accuracy, 98.23% CV score, 97.67% precision, 97.93% recall, 98.18% selectivity, 97.79% F1 score, 98.06% balanced accuracy, 98.05% G-mean, a 1.63% error rate, 0.98 AUC, and a standard deviation of 0.192. This study highlights the potential of ensemble ML approaches in improving the diagnosis of HCV. The findings provide a foundation for developing accurate predictive methods for HCV diagnosis.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0326488.t001（source: https://plos.figshare.com/articles/dataset/Dataset_variables_described_in_Raw_Form_/29420039）。
数据缺失？
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