Boosted neural network ensemble classification for lung cancer disease diagnosis
Boosted neural network ensemble classification for lung cancer disease diagnosis
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DOI：
10.1016/j.asoc.2019.04.031
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发表时间：
2019-07-01
影响因子：
8.7
通讯作者：
Thaventhiran, Chandrasekar
中科院分区：
文献类型：
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作者：
ALzubi, Jafar A.;Bharathikannan, Balasubramaniyan;Thaventhiran, Chandrasekar
肺癌疾病（LCD）的准确诊断是为肺癌患者提供及时治疗的重要过程。人工神经网络（ANN）是最近提出的机器学习（ML）算法，可用于大型和小型数据集。本文分析了大数据中 LCD 的权重优化神经网络与最大似然提升（WONN-MLB）的集成。该方法分为两个阶段，特征选择和集成分类。在第一阶段，使用集成的牛顿-拉夫森最大似然和最小冗余（MLMR）预处理模型来选择基本属性，以最大限度地减少分类时间。在第二阶段，应用增强加权优化神经网络集成分类算法对具有选定属性的患者进行分类，从而提高癌症疾病诊断的准确性并最大限度地减少假阳性率。实验结果表明，与传统技术相比，所提出的方法具有更好的误报率、预测准确性并减少了延迟。 (C) 2019 Elsevier B.V. 保留所有权利。
Accurate diagnosis of Lung Cancer Disease (LCD) is an essential process to provide timely treatment to the lung cancer patients. Artificial Neural Networks (ANN) is a recently proposed Machine Learning (ML) algorithm which is used on both large-scale and small-size datasets. In this paper, an ensemble of Weight Optimized Neural Network with Maximum Likelihood Boosting (WONN-MLB) for LCD in big data is analyzed. The proposed method is split into two stages, feature selection and ensemble classification. In the first stage, the essential attributes are selected with an integrated Newton-Raphsons Maximum Likelihood and Minimum Redundancy (MLMR) preprocessing model for minimizing the classification time. In the second stage, Boosted Weighted Optimized Neural Network Ensemble Classification algorithm is applied to classify the patient with selected attributes which improves the cancer disease diagnosis accuracy and also minimize the false positive rate. Experimental results demonstrate that the proposed approach achieves better false positive rate, accuracy of prediction, and reduced delay in comparison to the conventional techniques. (C) 2019 Elsevier B.V. All rights reserved.