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

Gang Huang, Hailun Wang, Lu Zhang and Muhammad Faisal Nadeem

Mathematical Problems in Engineering, 2022, vol. 2022, 1-12

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.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/8544122.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/8544122.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8544122

DOI: 10.1155/2022/8544122

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().