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Least absolute shrinkage and selection operator (LASSO)-logistic regression models were built utilizing extracted functions for predicting therapy reaction. The model performance was assessed with repeated 20 times stratified 4-fold cross-validation making use of receiver working feature (ROC) curves and contrasted with the corrected resampled t-test. RESULTS The model designed with hand-crafted features accomplished the mean location beneath the ROC curve (AUC) of 0.64, as the one built with DL-based functions
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