Using Classification and K-means Methods to Predict Breast Cancer Recurrence in Gene Expression Data

Mohammadreza Sehhati; Mohammad Amin Tabatabaiefar; Ali Haji Gholami; Mohammad Sattari

doi:10.4103/jmss.jmss_117_21

Using Classification and K-means Methods to Predict Breast Cancer Recurrence in Gene Expression Data

J Med Signals Sens. 2022 May 12;12(2):122-126. doi: 10.4103/jmss.jmss_117_21. eCollection 2022 Apr-Jun.

Authors

Mohammadreza Sehhati¹, Mohammad Amin Tabatabaiefar^{2

3}, Ali Haji Gholami⁴, Mohammad Sattari⁵

Affiliations

¹ Medical Image and Signal Processing Research Center, Department of Bioinformatics,School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
² Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
³ Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
⁴ Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran.
⁵ Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Abstract

Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data.

Method: The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k-means, hidden markov model, joint mutual information, neural network + k-means and suportvector machine + k-menas were implemented on 12172 genes and 200 samples.

Results: Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k-means get better performance than other techniques. The two techniques including neural network + k-means and random forest + k-means performed better than other techniques in identifying high risk cases.

Conclusion: Thirty of 12,172 genes are considered for classification that the use of clustering has improved the classification techniques performance.

Keywords: Classification; K-means; gene.