Table_1_PSO-XnB：预测CAD患者住院时长的模型数据（XLSX）

数据集获取（Figshare）

• 源站标题：Table_1_PSO-XnB: a proposed model for predicting hospital stay of CAD patients.XLSX
• DOI：10.3389/frai.2024.1381430.s001
• 发布日期：2024-05-03
• 文件数：1 个
• 文件总体积：10.91 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_PSO-XnB_a_proposed_model_for_predicting_hospital_stay_of_CAD_patients_XLSX/25746363

命令行下载

curl -L -o "Table_1_PSO-XnB: a proposed model for predicting hospital stay of CAD patients.XLSX" "https://ndownloader.figshare.com/files/46072248"

文件清单

• Table_1_PSO-XnB: a proposed model for predicting hospital stay of CAD patients.XLSX（10.91 KB）

文件总体积 10.91 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

Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model—a Particle Swarm Optimized-Enhanced NeuroBoost—that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimensionality of the data is reduced using deep neural autoencoders. The reconstructed data obtained from autoencoders is given as input to an eXtreme gradient boosting model. Finally, the model is tuned with particle swarm optimization to obtain optimal hyperparameters. With the proposed technique, the model achieved superior performance with an overall accuracy of 98.8% compared to traditional ensemble models and past research works. The model also scored highest in other metrics such as precision, recall, and particularly F1 scores for all categories of hospital stay. These scores validate the suitability of our proposed model in medical healthcare applications.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2024.1381430.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_PSO-XnB_a_proposed_model_for_predicting_hospital_stay_of_CAD_patients_XLSX/25746363）。