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Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapy
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International audience. As a pivotal task in cancer therapy, outcome prediction is the foundation for tailoring and adapting a treatment planning. In this paper, we propose to use image features extracted from PET and clinical characteristics. Considering that both information sources are imprecise or noisy, a novel prediction model based on Dempster-Shafer theory is developed. Firstly, a specific loss function with sparse regularization is designed for learning an adaptive dissimilarity metric between feature vectors of labeled patients. Through minimizing this loss function, a linear low-dimensional transformation of the input features is then achieved; meanwhile, thanks to the sparse penalty, the influence of imprecise input features can also be reduced via feature selection. Finally, the learnt dissimilarity metric is used with the Evidential K-Nearest-Neighbor (EK-NN) classifier to predict the outcome. We evaluated the proposed method on two clinical data sets concerning to lung and esophageal tumors, showing good performance.
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