使用堆叠集成模型和优化特征选择的宫颈癌诊断:一种可解释的人工智能方法

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers Pub Date : 2023-10-07 DOI:10.3390/computers12100200
Abdulaziz AlMohimeed, Hager Saleh, Sherif Mostafa, Redhwan M. A. Saad, Amira Samy Talaat
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引用次数: 0

摘要

子宫颈癌每年影响全世界50多万妇女,造成30多万人死亡。本文的主要目标是研究将特征选择方法与叠加模型应用于宫颈癌预测的效果,提出将不同模型与元学习器相结合的叠加集成学习用于宫颈癌预测,并利用可解释人工智能(explainable artificial intelligence, XAI)探索最优特征叠加模型的黑箱。使用了来自机器学习存储库(UCI)的高度不平衡且包含缺失值的宫颈癌数据集。因此,采用SMOTE-Tomek结合欠采样和过采样来处理不平衡数据,并实施预处理步骤来保留缺失值。贝叶斯优化对模型进行优化,选择最佳的模型体系结构。卡方评分、递归特征去除和基于树的特征选择是应用于数据集的三种特征选择技术。为了确定对宫颈癌预测最关键的因素,将堆叠模型扩展到多个级别:Level 1(多基础学习器)和Level 2(元学习器)。在第1层,使用堆叠(训练和测试堆叠)来组合多基模型的输出,而在第2层使用训练堆叠来训练元学习器模型。测试堆叠用于评估元学习器模型。结果表明,基于递归特征消除(RFE)选择的特征,叠加模型具有更高的准确率、精密度、召回率、f1-score和AUC。此外,为了保证模型的效率、有效性和可靠性,给出了局部和全局解释。
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Cervical Cancer Diagnosis Using Stacked Ensemble Model and Optimized Feature Selection: An Explainable Artificial Intelligence Approach
Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical cancer, propose stacking ensemble learning that combines different models with meta-learners to predict cervical cancer, and explore the black-box of the stacking model with the best-optimized features using explainable artificial intelligence (XAI). A cervical cancer dataset from the machine learning repository (UCI) that is highly imbalanced and contains missing values is used. Therefore, SMOTE-Tomek was used to combine under-sampling and over-sampling to handle imbalanced data, and pre-processing steps are implemented to hold missing values. Bayesian optimization optimizes models and selects the best model architecture. Chi-square scores, recursive feature removal, and tree-based feature selection are three feature selection techniques that are applied to the dataset For determining the factors that are most crucial for predicting cervical cancer, the stacking model is extended to multiple levels: Level 1 (multiple base learners) and Level 2 (meta-learner). At Level 1, stacking (training and testing stacking) is employed for combining the output of multi-base models, while training stacking is used to train meta-learner models at level 2. Testing stacking is used to evaluate meta-learner models. The results showed that based on the selected features from recursive feature elimination (RFE), the stacking model has higher accuracy, precision, recall, f1-score, and AUC. Furthermore, To assure the efficiency, efficacy, and reliability of the produced model, local and global explanations are provided.
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来源期刊
Computers
Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.40
自引率
3.60%
发文量
153
审稿时长
11 weeks
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