使用SFS和Boruta特征选择的机器学习预测多囊卵巢综合征：一种可解释的人工智能方法

数据集获取（Figshare）

• 源站标题：Prediction of polycystic ovary syndrome using machine learning with SFS and Boruta feature selection: an explainable AI approach
• DOI：10.6084/m9.figshare.30174247.v1
• 发布日期：2025-09-21
• 文件数：5 个
• 文件总体积：11.17 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Prediction_of_polycystic_ovary_syndrome_using_machine_learning_with_SFS_and_Boruta_feature_selection_an_explainable_AI_approach/30174247

命令行下载

curl -L -o "iaan_a_2560839_sm7386.tiff" "https://ndownloader.figshare.com/files/58122565"
curl -L -o "iaan_a_2560839_sm7385.tiff" "https://ndownloader.figshare.com/files/58122568"
curl -L -o "iaan_a_2560839_sm7384.docx" "https://ndownloader.figshare.com/files/58122571"
curl -L -o "iaan_a_2560839_sm7383.docx" "https://ndownloader.figshare.com/files/58122574"
curl -L -o "iaan_a_2560839_sm7382.docx" "https://ndownloader.figshare.com/files/58122577"

文件清单

• iaan_a_2560839_sm7386.tiff（4.71 MB）
• iaan_a_2560839_sm7385.tiff（4.22 MB）
• iaan_a_2560839_sm7384.docx（414.03 KB）
• iaan_a_2560839_sm7383.docx（14.02 KB）
• iaan_a_2560839_sm7382.docx（1.82 MB）

文件总体积 11.17 MB（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

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting numerous women of reproductive age, characterized by a variety of clinical and biochemical features. Accurate classification and diagnosis of PCOS remains challenging due to the heterogeneous nature of its manifestations. This study introduces a robust machine learning framework that combines a voting ensemble model with two distinct feature selection techniques, Sequential Forward Selection (SFS) and Boruta, to enhance the accuracy in classifying PCOS. We also utilized Explainable Artificial Intelligence (XAI) techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Partial Dependence Plot (PDP), AnchorTabular, and Permutation Importance, to interpret the ensemble model. These methods provide essential insights into the significance of key features for predicting PCOS patients. Results show that the proposed ensemble learning model achieved optimal performance with the feature selection technique used. Specifically, the proposed voting ensemble classifier and features picked by SFS had the highest accuracy among all models. This method can help in PCOS diagnosis and support early intervention.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30174247.v1（source: https://tandf.figshare.com/articles/dataset/Prediction_of_polycystic_ovary_syndrome_using_machine_learning_with_SFS_and_Boruta_feature_selection_an_explainable_AI_approach/30174247）。