运行时计算得分分析

数据集获取（Figshare）

• 源站标题：The runtime computational score analysis.
• DOI：10.1371/journal.pone.0312313.t008
• 发布日期：2024-11-01
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_runtime_computational_score_analysis_/27470386

命令行下载

curl -L -o "Table 8.xls" "https://ndownloader.figshare.com/files/50151751"

文件清单

• Table 8.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyroid syndrome extend beyond the thyroid itself, affecting metabolism, energy levels, and overall well-being. Thyroid syndrome is associated with severe cases of thyroid dysfunction, highlighting the potentially life-threatening consequences of untreated or inadequately managed thyroid disorders. This research aims to propose an advanced meta-learning approach for the timely detection of Thyroid syndrome. We used a standard thyroid-balanced dataset containing 7,000 patient records to apply advanced machine-learning methods. We proposed a novel meta-learning model based on a unique stack of K-Neighbors (KN) and Random Forest (RF) models. Then, a meta-learning Logistic Regression (LR) model is built based on the collective experience of stacked models. For the first time, the novel proposed KRL (KN-RF-LR) method is employed for the effective diagnosis of Thyroid syndrome. Extensive research experiments illustrated that the novel proposed KRL outperformed state-of-the-art approaches, achieving an impressive performance accuracy of 98%. We vindicated the performance scores through k-fold cross-validation and enhanced performance using hyperparameter tuning. Our research revolutionized the timely detection of thyroid syndrome, contributing to the enhancement of human life by reducing thyroid mortality rates.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0312313.t008（source: https://plos.figshare.com/articles/dataset/The_runtime_computational_score_analysis_/27470386）。