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ORIGINAL ARTICLE
Year : 2022  |  Volume : 12  |  Issue : 2  |  Page : 122-126

Using classification and K-means methods to predict breast cancer recurrence in gene expression data


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; Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
3 Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
4 Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Mohammad Sattari
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.jmss_117_21

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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.


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