胎儿酒精谱系障碍早期诊断机器学习算法数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.docx
• DOI：10.3389/fnins.2024.1400933.s001
• 发布日期：2024-05-06
• 文件数：1 个
• 文件总体积：24.51 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_algorithms_to_the_early_diagnosis_of_fetal_alcohol_spectrum_disorders_docx/25755216

命令行下载

curl -L -o "Data_Sheet_1_Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.docx" "https://ndownloader.figshare.com/files/46118637"

文件清单

• Data_Sheet_1_Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.docx（24.51 KB）

文件总体积 24.51 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

IntroductionFetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders.
MethodsThis study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions.
ResultsThis study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems.
DiscussionOur finding

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2024.1400933.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_algorithms_to_the_early_diagnosis_of_fetal_alcohol_spectrum_disorders_docx/25755216）。