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



Sehhati M1 ; Tabatabaiefar M2, 3 ; Gholami A4 ; Sattari M5
Authors
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Authors Affiliations
  1. 1. Medical Image and Signal Processing Research Center, Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
  5. 5. Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2022


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. © 2022 Isfahan University of Medical Sciences(IUMS). All rights reserved.
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