شنبه 7 تیر 1404
دوره 15، شماره 3 - ( 4-1404 )
جلد 15 شماره 3 صفحات 0-0 |
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Fasihi L, Agha-Alinejad H, Gharakhanlou R, J. Amaro Gahete F. Comparison and Prediction of Breast Cancer Using Discriminant Analysis Algorithm in Active and Inactive Women. PTJ 2025; 15 (3)
URL: http://ptj.uswr.ac.ir/article-1-708-fa.html
URL: http://ptj.uswr.ac.ir/article-1-708-fa.html
Comparison and Prediction of Breast Cancer Using Discriminant Analysis Algorithm in Active and Inactive Women. فصلنامه فیزیک درمانی. 1404; 15 (3)
چکیده: (452 مشاهده)
Purpose: One of the most common types of malignant cancer in women is breast cancer, which has been increasing in recent years. The presence of various symptoms and characteristics of this disease makes diagnosis difficult for doctors. Data mining provides the possibility of analyzing clinical data of patients for medical decision-making. The aim of this article was to compare and predict breast cancer using the differential analysis algorithm in active and inactive women.
Methods: The medical records of 1782 women suspected of having breast cancer constituted the statistical population of this study. After reviewing the files, 642 medical records (329 active women and 313 inactive women) containing laboratory, anthropometric and demographic information were selected as samples. The differential analysis algorithm and 15 effective features of breast cancer were used to predict the disease. Statistical analyses were performed using MATLAB software.
Results: The results showed that using 15 risk factors, the differential analysis algorithm has an accuracy (79.7%) and precision (77.5%) in predicting breast cancer in active women and an accuracy (71.6%) and precision (69.3%) in inactive women. The results also show that the differential analysis algorithm has a better performance in predicting breast cancer in active women.
Conclusion: Given the high accuracy and precision of the differential analysis algorithm in predicting breast cancer, doctors and specialists in the treatment department can use this algorithm in medical and therapeutic centers to predict this type of cancer.
Methods: The medical records of 1782 women suspected of having breast cancer constituted the statistical population of this study. After reviewing the files, 642 medical records (329 active women and 313 inactive women) containing laboratory, anthropometric and demographic information were selected as samples. The differential analysis algorithm and 15 effective features of breast cancer were used to predict the disease. Statistical analyses were performed using MATLAB software.
Results: The results showed that using 15 risk factors, the differential analysis algorithm has an accuracy (79.7%) and precision (77.5%) in predicting breast cancer in active women and an accuracy (71.6%) and precision (69.3%) in inactive women. The results also show that the differential analysis algorithm has a better performance in predicting breast cancer in active women.
Conclusion: Given the high accuracy and precision of the differential analysis algorithm in predicting breast cancer, doctors and specialists in the treatment department can use this algorithm in medical and therapeutic centers to predict this type of cancer.
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