模型评估结果（结核病治疗不依从预测）
包含838名结核病患者回顾性数据的表格，用于训练五种机器学习模型预测治疗不依从性并识别风险因素。
基本信息
资源简介
模型评估结果（结核病治疗不依从预测）
【数据集背景】
本数据集源站标题为《Evaluation results of the developed models.》。源站首次发布：2023-07-03。
【数据内容】
源站原始描述：Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pgph.0001466.t002。
【主题与分类】
源站学科分类：Medicine、Biotechnology、Sociology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：xlink "> despite、upper arm circumference、targeting suited interventions、specifically classification types、receiver operating curve、positive sputum test、machine learning techniques、machine learning approach、known efficacious interventions、evaluation parameter ).。
【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/Evaluation_results_of_the_developed_models_/23619157
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Gichuhi, Haron W.; Magumba, Mark; Kumar, Manish; Mayega, Roy William (2023). Evaluation results of the developed 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：Evaluation results of the developed models.
- DOI：10.1371/journal.pgph.0001466.t002
- 发布日期：2023-07-03
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Evaluation_results_of_the_developed_models_/23619157
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/41443954"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pgph.0001466.t002（source: https://plos.figshare.com/articles/dataset/Evaluation_results_of_the_developed_models_/23619157）。
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。
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