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Volume 32, Issue 154 (September & October 2024)
J Adv Med Biomed Res 2024, 32(154): 350-360 |
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Bagherian H, Haghjoo S, Mosayebi A, noorshargh P, Arabzades S, Sharifi M et al . Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques. J Adv Med Biomed Res 2024; 32 (154) :350-360
URL: http://journal.zums.ac.ir/article-1-7417-en.html
URL: http://journal.zums.ac.ir/article-1-7417-en.html
Hossein Bagherian *1 
, Shagayegh Haghjoo2 , Azam Mosayebi2 , Pegah Noorshargh2 , Saeedeh Arabzades2 , Mehran Sharifi3 , Mohammad Sattari4
, Shagayegh Haghjoo2 , Azam Mosayebi2 , Pegah Noorshargh2 , Saeedeh Arabzades2 , Mehran Sharifi3 , Mohammad Sattari4
1- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran , h_bagherian@yahoo.com
2- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
3- Cancer Prevention Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
4- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
2- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
3- Cancer Prevention Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
4- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract: (161 Views)
Background & Objective: Breast cancer is a leading cause of female mortalities worldwide. This study has used machine learning techniques to determine the most critical factors influencing the survival rate of breast cancer patients in Isfahan.
Materials & Methods: A list of variables influencing the survival of breast cancer patients was initially extracted from the data sets of two Isfahan hospitals for this analytical investigation, leading to the extraction of 16 critical factors based on the opinions of oncologists. In the next step, the missing values were identified and deleted or corrected, followed by converting some features into numerical ranges. Ultimately, the key variables influencing the survival rate of breast cancer patients were determined by applying 11 machine learning algorithms.
Results: Forward selection is more accurate than other techniques. Of the 15 input features, 13 were extracted as influential survival rates at least once using different techniques, with BC-ER-PR-HER2 ranking first among the features. The six first features, including Bc-ER-PR-HER2, lymph node dissection, behavior, primary surgery procedure, the exact number of nodes examined, and the exact number of positive nodes, were determined as the best combination for identifying breast cancer patients. Even though cancer behavior patterns differ in various societies, there are still similarities in risk factors.
Conclusion: Forward selection combined with principal component analysis using support vector machines, neural networks, and random forests can be the best model for breast cancer prediction. Neural networks, random forests, and support vector machines are very good at predicting breast cancer survival.
Materials & Methods: A list of variables influencing the survival of breast cancer patients was initially extracted from the data sets of two Isfahan hospitals for this analytical investigation, leading to the extraction of 16 critical factors based on the opinions of oncologists. In the next step, the missing values were identified and deleted or corrected, followed by converting some features into numerical ranges. Ultimately, the key variables influencing the survival rate of breast cancer patients were determined by applying 11 machine learning algorithms.
Results: Forward selection is more accurate than other techniques. Of the 15 input features, 13 were extracted as influential survival rates at least once using different techniques, with BC-ER-PR-HER2 ranking first among the features. The six first features, including Bc-ER-PR-HER2, lymph node dissection, behavior, primary surgery procedure, the exact number of nodes examined, and the exact number of positive nodes, were determined as the best combination for identifying breast cancer patients. Even though cancer behavior patterns differ in various societies, there are still similarities in risk factors.
Conclusion: Forward selection combined with principal component analysis using support vector machines, neural networks, and random forests can be the best model for breast cancer prediction. Neural networks, random forests, and support vector machines are very good at predicting breast cancer survival.
Type of Study: Original Article |
Subject:
Health improvement strategies
Received: 2024/01/2 | Accepted: 2024/12/9 | Published: 2024/10/19
Received: 2024/01/2 | Accepted: 2024/12/9 | Published: 2024/10/19
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