与先前研究的性能比较
基于UCI甲状腺疾病数据集，包含9172个样本和30个特征，用于甲状腺疾病诊断，并提出了自堆叠分类器。
基本信息
资源简介
与先前研究的性能比较
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 thorough comparative assessment、stack ensemble model、significant health risk、increasing treatment costs、increased binding protein，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Performance comparison with previous study.》。源站首次发布：2024-01-03。
【数据内容】
源站原始描述：Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous stud…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 14.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0295501.t014。
【主题与分类】
源站学科分类：Biochemistry、Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer。主题标签：thorough comparative assessment、stack ensemble model、significant health risk、increasing treatment costs、increased binding protein、ensemble voting classifiers、deep neural networks、diagnose primary hypothyroidism、predicting thyroid disease、proposed approach demonstrates。
【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/Performance_comparison_with_previous_study_/24938017
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Ji, Shengjun (2024). Performance comparison with previous study.. PLOS ONE. Dataset
下载信息
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Performance comparison with previous study.
- DOI：10.1371/journal.pone.0295501.t014
- 发布日期：2024-01-03
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Performance_comparison_with_previous_study_/24938017
命令行下载
curl -L -o "Table 14.xls" "https://ndownloader.figshare.com/files/43896796"
文件清单
Table 14.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0295501.t014（source: https://plos.figshare.com/articles/dataset/Performance_comparison_with_previous_study_/24938017）。
数据缺失？
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