Journal of medical signals and sensors · 2022
Using Classification and K-means Methods to Predict Breast Cancer Recurrence in Gene Expression Data.
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Sehhati Mohammadreza, Tabatabaiefar Mohammad Amin, Gholami Ali Haji, Sattari Mohammad
Аннотация
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.
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.