Volume 6, Issue 4 (3-2020)
jhbmi 2020, 6(4): 309-319 |
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Sadeghi S, Golabpour A. An Algorithm for Predicting Recurrence of Breast Cancer Using Genetic Algorithm and Nearest Neighbor Algorithm. jhbmi 2020; 6 (4) :309-319
URL: http://jhbmi.ir/article-1-366-en.html
URL: http://jhbmi.ir/article-1-366-en.html
Ph.D. in Medical Informatics, Assistant Professor, Shahroud University of Medical Sciences, School of Paramedical, Shahroud, Iran
Abstract: (3928 Views)
Introduction: Breast cancer is one of the most common types of cancer and the most common type of malignancy in women, which has been growing in recent years. Patients with this disease have a chance of recurrence. Many factors reduce or increase this probability. Data mining is one of the methods used to detect or anticipate cancers, and one of its most common uses is to predict the recurrence of breast cancer.
Cases and Methods: Out of 699 patients with breast cancer, 458 (66%) of them did not relapse and 241 (34%) of their cancer recurred. This information was collected from 91 to 94 years of history of patients with breast cancer in the academic Jihad. In this study, the combination of two nearest neighboring algorithms and a genetic algorithm are proposed to predict the relapse of patients with breast cancer. First, the nearest neighboring algorithm is presented to predict the recurrence of breast cancer. Then, using the genetic algorithm, the dependent variables are reduced to make the model more appropriate.
Results: The number of dependent variables is 14 variables, which is reduced by 6 genetic algorithms to better predict the model. To evaluate the model, the health parameter is used, which is 77.14% for the proposed model, which could not be more suitable for other methods.
Conclusion: In this study, the proposed algorithm was examined with other predictive methods and it was determined that the proposed algorithm is better.
Cases and Methods: Out of 699 patients with breast cancer, 458 (66%) of them did not relapse and 241 (34%) of their cancer recurred. This information was collected from 91 to 94 years of history of patients with breast cancer in the academic Jihad. In this study, the combination of two nearest neighboring algorithms and a genetic algorithm are proposed to predict the relapse of patients with breast cancer. First, the nearest neighboring algorithm is presented to predict the recurrence of breast cancer. Then, using the genetic algorithm, the dependent variables are reduced to make the model more appropriate.
Results: The number of dependent variables is 14 variables, which is reduced by 6 genetic algorithms to better predict the model. To evaluate the model, the health parameter is used, which is 77.14% for the proposed model, which could not be more suitable for other methods.
Conclusion: In this study, the proposed algorithm was examined with other predictive methods and it was determined that the proposed algorithm is better.
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