随机森林超参数优化数据集
基于墨西哥COVID-19临床表格数据，利用随机森林及超参数优化进行呼吸疾病诊断预测的数据集。
基本信息
资源简介
随机森林超参数优化数据集
【数据集背景】
本数据集源站标题为《Random forest with hyperparameter optimization.》。源站首次发布：2024-12-02。
【数据内容】
源站原始描述：In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optim…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 5.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0308015.t005。
【主题与分类】
源站学科分类：Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer、Science Policy。主题标签：shapely adaptive explanations、results obtained show、deadliest respiratory diseases、causes serious damage、superior prediction accuracy、adaboost algorithm outperformed、respiratory disease prediction、machine learning algorithms、incorporating hyperparameter optimization、machine learning。
【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/Random_forest_with_hyperparameter_optimization_/27946240
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Kaur, Balraj Preet; Singh, Harpreet; Hans, Rahul; Sharma, Sanjeev Kumar; Sharma, Chetna; Hassan, Md. Mehedi (2024). Random forest with hyperparameter optimization.. 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：Random forest with hyperparameter optimization.
- DOI：10.1371/journal.pone.0308015.t005
- 发布日期：2024-12-02
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Random_forest_with_hyperparameter_optimization_/27946240
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/50925406"
文件清单
Table 5.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0308015.t005（source: https://plos.figshare.com/articles/dataset/Random_forest_with_hyperparameter_optimization_/27946240）。
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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