Fri, Jul 18, 2025
Volume 15, Issue 3 (Summer 2025)
PTJ 2025, 15(3): 243-252 |
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Fasihi L, Agha-Alinejad H, Gharakhanlou R, Gahete F J A. Comparison and Prediction of Breast Cancer Using Discriminant Analysis Algorithm in Active and Inactive Women. PTJ 2025; 15 (3) :243-252
URL: http://ptj.uswr.ac.ir/article-1-708-en.html
URL: http://ptj.uswr.ac.ir/article-1-708-en.html
1- Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran.
2- Department of Physiology, Faculty of Medical Sciences, University of Granada, Granada, Spain.
2- Department of Physiology, Faculty of Medical Sciences, University of Granada, Granada, Spain.
Abstract: (557 Views)
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, version 2024.
Results: The results showed that using 15 risk factors, the differential analysis algorithm has an accuracy of 79.7% and a precision of 77.5% in predicting breast cancer in active women, while it achieved an accuracy of 71.6% and a precision of 69.3% in inactive women. The results also showed that the differential analysis algorithm performs better 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, version 2024.
Results: The results showed that using 15 risk factors, the differential analysis algorithm has an accuracy of 79.7% and a precision of 77.5% in predicting breast cancer in active women, while it achieved an accuracy of 71.6% and a precision of 69.3% in inactive women. The results also showed that the differential analysis algorithm performs better 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.
Type of Study: Research |
Subject:
General
Received: 2024/12/16 | Accepted: 2025/01/22 | Published: 2025/07/13
Received: 2024/12/16 | Accepted: 2025/01/22 | Published: 2025/07/13
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