分类器性能（肝脏疾病预测）

数据集获取（Figshare）

• 源站标题：Classifier performance.
• DOI：10.1371/journal.pone.0330899.t005
• 发布日期：2025-08-28
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Classifier_performance_/30004143

命令行下载

curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/57485814"

文件清单

• Table 5.xls（5.63 KB）

文件总体积 5.63 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

Liver disease encompasses a range of conditions affecting the liver, including hepatitis, cirrhosis, fatty liver, and liver cancer. It can be caused by infections, alcohol abuse, obesity, or genetic factors, and it often progresses silently until advanced stages. Early detection and lifestyle adjustments are essential for effective management and to prevent severe liver damage. This study explores the application of machine learning (ML) techniques to predict liver disease, leveraging a dataset to compare the performance of several ensemble classifiers. The algorithms include the Random Forrest Classifier, Ada Boost Classifier, and Gradient Boosting Classifier. After a series of feature extraction and selection, hyperparameter tuning by Randomized Search CV and GridSearchCV, we aimed to determine the best model for liver disease prediction in terms of accuracy, precision, recall, and F1-score. The results showed that the Random Forest Classifier, optimized with GridSearchCV, achieved the highest accuracy at just over 85.17%. The considerations presented in this classifier can be considered for potential use as a precise diagnostic tool for liver disease diagnostics as these measurements indicate that this classifier works balanced with precision at 0.85 for both the presence and absence of the given disease as well as recall of 0.81 for its presence and 0.87 for its absence and F1-measure of 0.83 and 0.85 respectively. There were also relatively high performances of AdaBoost Classifier and Gradient Boosting Classifier, though none of the classifiers outperformed Random Forest Classifier significantly. The research has shown the potential of ensemble ML techniques, especially in the diagnosis of medical conditions, including liver diseases which, if diagnosed early, are critical. The results add evidence regarding the applicability of the ML models in clinical practices with the potential to improve diagnostic activities and consequently the outcomes of patients. Future studies will build on these models, testing them on larger and more diverse sets of data, including aspects of deep learning, and apply the research to other disease domains. The work presented in

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0330899.t005（source: https://plos.figshare.com/articles/dataset/Classifier_performance_/30004143）。