基于KNN插补的机器学习模型结果（宫颈癌）
宫颈癌检测机器学习模型结果，采用KNN插补处理缺失值，表格数据，用于分类任务。
基本信息
资源简介
基于KNN插补的机器学习模型结果（宫颈癌）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 women &# 8217、third employing pca、machine learning models、offering medical experts、manage missing values，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Results of the ML models using KNN imputer.》。配套论文《Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning》发表于 PLOS ONE（2024），DOI：10.1371/journal.pone.0295632。源站首次发布：2024-01-03。
【数据内容】
源站原始描述：Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervic…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 5.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0295632.t005。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：women &# 8217、third employing pca、machine learning models、offering medical experts、manage missing values、imputing missing values、handling missing data、datasets commonly used、cervical cancer detection、achieving high accuracy。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Results_of_the_ML_models_using_KNN_imputer_/24937928
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Aljrees, Turki (2024). Results of the ML models using KNN imputer.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Results of the ML models using KNN imputer.
- DOI：10.1371/journal.pone.0295632.t005
- 发布日期：2024-01-03
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Results_of_the_ML_models_using_KNN_imputer_/24937928
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/43896701"
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
Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0295632.t005（source: https://plos.figshare.com/articles/dataset/Results_of_the_ML_models_using_KNN_imputer_/24937928）。
数据缺失？
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