多分类器预测膝骨关节炎进展实验结果

数据集获取（Figshare）

• 源站标题：Results of machine learning experiments for “Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data”
• DOI：10.25405/data.ncl.10043060
• 发布日期：2019-10-30
• 文件数：1 个
• 文件总体积：334.75 MB
• 源站页面：https://data.ncl.ac.uk/articles/dataset/_Multi-classifier_prediction_of_knee_osteoarthritis_progression_from_incomplete_imbalanced_longitudinal_data_-_results_of_machine_learning_experiments/10043060

命令行下载

curl -L -o "experiments.tar" "https://ndownloader.figshare.com/files/18105551"

文件清单

• experiments.tar（334.75 MB）

文件总体积 334.75 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

The archive file includes results of machine learning experiments performed for the article “Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data”. The hypothesis of the article is that prediction models trained on historical data will be more effective at identifying fast progressing knee OA patients than conventional inclusion criteria.
For all experiments the first level folder hierarchy indicates the method used. Where parameter tuning is performed, the second level folders indicate algorithm parameters. Each experiment output is stored in a xz compressed text file in JSON format.
In experiments measuring the learning curves (training-), each results file describes: experiment setup (algorithm, number of subsets, down-sampled class size)

• list of training set sizes* performance measure statistics for all subsets at each training size (flat list) including min, median and max score, and median deviation from median (mad), given for both test and training set instancesIn parameter tuning experiments (prediction-multi-), each results file contains: experiment setup (method / algorithm, number of CV repeats, number of model runs)
• imputer parameters (not important, kept constant in all experiments)* classifier parameters (for random forest)* true class for each instance* class predictions by the median model from each CV-repeat* class probabilities estimated by the median model from each CV-repeat* performance measure statistics for each CV-repeat including min, median and max score, and median deviation from median (mad)In RFE experiments (prediction-multi-rfe-) the results additionally include: scores for all RFE steps for each CV-repeat* number of times each feature was selected (across all folds and CV-repeats)

许可

CC0

引用

学术使用请引用 DOI 10.25405/data.ncl.10043060（source: https://data.ncl.ac.uk/articles/dataset/_Multi-classifier_prediction_of_knee_osteoarthritis_progression_from_incomplete_imbalanced_longitudinal_data_-_results_of_machine_learning_experiments/10043060）。