A1076
Title: CNN-based deep learning approaches for high-dimensional survival data
Authors: Il Do Ha - Pukyong National University (Korea, South) [presenting]
Abstract: Deep learning (DL) includes various architectures, such as deep neural networks (DNN) and convolutional neural networks (CNN). DL is very powerful and flexible for non-tabular (non-structured) data (e.g., image, text). However, in tabular data, standard DNNs often do not outperform traditional machine learning (ML) methods such as tree-based models (e.g., random forest, XGBoost). CNNs carry out dimensionality reduction for non-tabular (especially image) data but may be useful in tabular data, too. In this talk, our purpose is to present CNN-based approaches for high-dimensional survival tabular data, which provides an end-to-end learning framework. The predictive performance of the proposed method is evaluated by comparing it with existing machine learning/DL methods using real high-dimensional survival data.
