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

Summary: Can gene expression data predict breast cancer recurrence? Research shows combining clustering with machine learning identifies high-risk cases more efficiently. #BreastCancer #Bioinformatics

Sehhati M¹ ; Tabatabaiefar M^{2, 3} ; Gholami A⁴ ; Sattari M⁵
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.