Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

Abstract

Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection and a subsequent classification using latent information. Precisely, a typical autoencoder architecture is employed for reconstructing the original input. Then the sparse coding technique is applied to optimize the network structure, with the aim of selecting optimal features and enhancing the generalization capability. In addition, the refined latent information is further cast as alternative features for training a sparse classifier. To evaluate the performance of the proposed autoencoder architecture, we present a comprehensive analysis using a publicly available data repository (i.e., Surveillance, Epidemiology, and End Results, SEER). Experimental study shows that the proposed approach has the ability of extracting important features from high-dimensional inputs and achieves competitive performance than other state-of-the-art classification techniques.

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

“…By contrast, the suggested way has a relatively fixed performance from the training and testing sets. Finally, it is empirically confirmed that the presented method leads to an essential improvement compared to other methods in forecast accuracy [43].…”

“…This paper [43] expresses a new autoencoder method corresponding to sparse coding. The vital contribution is sparsity coding for input feature selection and a subsequent classification utilising latent information.…”

A Comprehensive Review of Healthcare Prediction using Data Science with Deep Learning

“…However, in the area of healthcare prediction, it has not yet been completely utilized. Six sample deep architectures-Deep Belief Network (DBN) [27], Convolutional Neural Network (CNN) [28], Recurrent Neural Network (RNN) [29], Long Short-Term Memory (LSTM) [30], Auto-encoder [31], and Sparse auto encoder [32]-were primarily the focus on the published research on DL. Based on these six exemplary deep architectures, this section aims to examine existing techniques.…”