Chen Xiaoyuan, Aljrees Turki, Umer Muhammad, Saidani Oumaima, Almuqren Latifah, Mzoughi Olfa, Ishaq Abid, Ashraf Imran
Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, P.R. China.
Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
Digit Health. 2023 Oct 3;9:20552076231203802. doi: 10.1177/20552076231203802. eCollection 2023 Jan-Dec.
Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models.
In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values.
The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies.
This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.
宫颈癌是发展中国家女性死亡的主要原因之一。为确保减少其不良后果，首要遵循的方案包括在专业医学专家的指导下进行早期检测和治疗。识别这种恶性肿瘤的一种有效方法是检查巴氏涂片图像。然而，在宫颈癌检测自动化的背景下，许多现有数据集经常出现数据点缺失的情况，这一因素会严重影响机器学习模型的有效性。
针对这些障碍，本研究引入了一个自动化系统，旨在双重聚焦预测宫颈癌：巧妙管理缺失数据并同时实现显著的准确性。该系统的核心基于一个堆叠集成投票分类器模型，该模型融合了三种不同的机器学习模型，所有这些模型都与K近邻插补器和谐集成，以解决缺失值问题。
在采用K近邻插补方法时，提出的模型实现了99.41%的准确率、97.63%的精确率、95.96%的召回率和96.76%的F1分数。该调查进行了一项比较分析，在两种情况下将该模型的性能与七种替代机器学习算法进行对比：一种是消除缺失值的情况，另一种是采用K近邻插补的情况。与当前最先进的方法相比，本研究验证了所提出模型的有效性。
本研究深入探讨了用于宫颈癌检测的数据集中处理缺失数据的挑战。这些发现有可能帮助医疗保健专业人员实现早期检测，并提高为受宫颈癌影响的个体提供的护理质量。
