Decision rules 数据集
基于CBC数据的β-地中海贫血携带者分类数据集，用于优势粗糙集模型训练与评估。
基本信息
资源简介
Decision rules 数据集
【数据集背景】
本数据集源站标题为《Decision rules.》。源站首次发布：2025-09-18。
【数据内容】
源站原始描述：Thalassemia is an inherited blood disorder and is among the five most prevalent birth-related complications, especially in Southeast Asia. Thalassemia is classified into two main types—alpha-thalassemia and beta-thalassemia—based on the reduced or absent production of the corresponding globin chains. Over the past couple of decades, researchers have increasingly focused on the application of machine learning algorithms to medical data for identifying hidden patterns to assist in the prediction a…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 6.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0331985.t006。
【主题与分类】
源站学科分类：Genetics、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：support vector machine、inherited blood disorder、identifying hidden patterns、deep neural networks、corresponding globin chains、machine learning algorithms、accurately detect carriers、xlink "> thalassemia、thalassemia &# 8212、successfully identified beta。
【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/Decision_rules_/30159938
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Chishti, Saim; Nosheen, Faryal; Fatima, Joddat; Sultan, Nadia; Khalid, Madiha (2025). Decision rules.. 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：Decision rules.
- DOI：10.1371/journal.pone.0331985.t006
- 发布日期：2025-09-18
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Decision_rules_/30159938
命令行下载
curl -L -o "Table 6.xls" "https://ndownloader.figshare.com/files/58080320"
文件清单
Table 6.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Thalassemia is an inherited blood disorder and is among the five most prevalent birth-related complications, especially in Southeast Asia. Thalassemia is classified into two main types—alpha-thalassemia and beta-thalassemia—based on the reduced or absent production of the corresponding globin chains. Over the past couple of decades, researchers have increasingly focused on the application of machine learning algorithms to medical data for identifying hidden patterns to assist in the prediction and classification of diseases and patients. To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. Many researchers have employed different artificial intelligence-based algorithms, i.e., Random Forest, Decision Tree, Support Vector Machine, ensemble-based classifiers, and deep neural networks to accurately detect carriers of beta-thalassemia by training on both diseased and normal test reports. While genetic testing is required by doctors for the most accurate diagnosis, a simple Complete Blood Count (CBC) report can be used to estimate the likelihood of being a beta-thalassemia carrier. Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. They achieve high accuracy but at the cost of substantial changes to the dataset through class normalization. In this research, we have proposed a Dominance-based Rough Set Approach model to classify patients without balancing the classes (Normal, Abnormal), and the model achieved good performance (91% accuracy). In terms of generalization, the proposed model obtained 89% accuracy on unseen data, comparable to or better than existing approaches.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0331985.t006（source: https://plos.figshare.com/articles/dataset/Decision_rules_/30159938）。
数据缺失？
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