基于机器学习的正常认知功能人群轻度认知障碍预测数据集

数据集获取（Figshare）

• 源站标题：Table_1_Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.DOCX
• DOI：10.3389/fneur.2024.1352423.s001
• 发布日期：2024-02-02
• 文件数：1 个
• 文件总体积：23.03 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning-based_prediction_of_mild_cognitive_impairment_among_individuals_with_normal_cognitive_function_DOCX/25134377

命令行下载

curl -L -o "Table_1_Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.DOCX" "https://ndownloader.figshare.com/files/44375879"

文件清单

• Table_1_Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.DOCX（23.03 KB）

文件总体积 23.03 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

BackgroundPrevious studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations.
MethodsThe data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machine-learning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables.
ResultsAmong these machine learning models; eXtreme Gradient Boosting (XGB) was the best classification model. The classification accuracy of clinical variables was 65.90%, of image variables was 79.54%, of a combination of clinical and image variables was 94.32%. The best result of the combination was an accuracy of 94.32%, a precision of 96.21%, and a recall of 93.08%. XGB with a combination of clinical and image variables had a potential prospect for the risk prediction of MCI. From clinical perspective, the degree of white matter hyperintensity (WMH), especially in the frontal lobe, and the control of systolic blood pressure (SBP) were the most important risk factor for the development of MCI.
ConclusionThe best MCI classification results came from the XGB model with a combination of both clinical and imaging variables. The degree of WMH in the frontal lobe and SBP control were the most important variables in predicting MCI.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2024.1352423.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning-based_prediction_of_mild_cognitive_impairment_among_individuals_with_normal_cognitive_function_DOCX/25134377）。