基于集成机器学习框架的血红蛋白贫血预测数据集

数据集获取（Figshare）

• 源站标题：An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers
• DOI：10.6084/m9.figshare.27325503.v1
• 发布日期：2024-10-29
• 文件数：3 个
• 文件总体积：355.36 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/An_ensemble_machine_learning_framework_with_explainable_artificial_intelligence_for_predicting_haemoglobin_anaemia_considering_haematological_markers/27325503

命令行下载

curl -L -o "tssc_a_2420927_sm3000.csv" "https://ndownloader.figshare.com/files/50061516"
curl -L -o "tssc_a_2420927_sm2999.docx" "https://ndownloader.figshare.com/files/50061519"
curl -L -o "tssc_a_2420927_sm2998.pdf" "https://ndownloader.figshare.com/files/50061522"

文件清单

• tssc_a_2420927_sm3000.csv（274.79 KB）
• tssc_a_2420927_sm2999.docx（19.80 KB）
• tssc_a_2420927_sm2998.pdf（60.76 KB）

文件总体积 355.36 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

Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models’’ judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.27325503.v1（source: https://tandf.figshare.com/articles/dataset/An_ensemble_machine_learning_framework_with_explainable_artificial_intelligence_for_predicting_haemoglobin_anaemia_considering_haematological_markers/27325503）。