Diagnosis of breast cancer with Stacked autoencoder and Subspace kNN

Kemal Adem

Physica A: Statistical Mechanics and its Applications, 2020, vol. 551, issue C

Abstract: Breast cancer is one of the most common and deadliest cancer types in women worldwide. Research on this disease has become very important because early diagnosis stages, clinical applications and the speed of response to treatment are facilitated in diseases such as cancer. In this study, an approach is proposed in which a Subspace kNN algorithm is used together with Stacked autoencoder (SAE) for diagnosis of disease on the breast cancer microarray dataset for the first time. Such hybrid approaches can provide better results when classifying data sets with high-dimensional and uncertainty. The data set used in the study was taken from Kent Ridge-2 database. It consists of 97 samples (51 benign, 46 malicious) and 24482 attributes. The performance of the proposed method was evaluated and the results were compared with other well-known methods of dimension reduction and machine learning. As a result of the comparison, the data set was reduced to 100 attributes by using SAE and Subspace kNN and 91.24% accuracy was achieved. The result obtained provides important classification accuracy, especially in high-dimensional data sets. The importance of this study is that the models that were created by using various classifiers to increase the success rate of the stacked autoencoder-softmax classifier model in the breast cancer microarray data set were applied for the first time. In this regard, it is considered that automation-based studies will provide diagnostic decision support system a solution using the proposed method in future works.

Keywords: Breast cancer; Stacked autoencoder; Subspace kNN (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437120302831
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:551:y:2020:i:c:s0378437120302831

DOI: 10.1016/j.physa.2020.124591

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().