逻辑回归超参数搜索空间
用于糖尿病多分类逻辑回归模型的超参数搜索空间数据集，基于医疗城市医院实验室数据。
基本信息
资源简介
逻辑回归超参数搜索空间
【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 svm classifier achieves、moderately imbalanced based、various body systems、impaired glucose tolerance、multiclass diabetes mellitus，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《Hyperparameter search space for LR.》。源站首次发布：2024-05-16。
【数据内容】
源站原始描述：Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar…
【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.0300785.t005。
【主题与分类】
源站学科分类：Biochemistry、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：svm classifier achieves、moderately imbalanced based、various body systems、impaired glucose tolerance、multiclass diabetes mellitus、produce enough insulin、nearest neighbor provides、extremely imbalanced laboratory、achieved maximum accuracy、classification approach using。
【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/Hyperparameter_search_space_for_LR_/25840586
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Sahid, Md Abdus; Babar, Mozaddid Ul Hoque; Uddin, Md Palash (2024). Hyperparameter search space for LR.. 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：Hyperparameter search space for LR.
- DOI：10.1371/journal.pone.0300785.t005
- 发布日期：2024-05-16
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Hyperparameter_search_space_for_LR_/25840586
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/46377515"
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
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection—filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed cond
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0300785.t005（source: https://plos.figshare.com/articles/dataset/Hyperparameter_search_space_for_LR_/25840586）。
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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