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- W2943040914 abstract "Abstract 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." @default.
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- W2943040914 date "2019-07-01" @default.
- W2943040914 modified "2023-10-06" @default.
- W2943040914 title "Boosted neural network ensemble classification for lung cancer disease diagnosis" @default.
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- W2943040914 doi "https://doi.org/10.1016/j.asoc.2019.04.031" @default.
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