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

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.