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Updated: Sep 3, 2025
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
Published on: February 6, 2020
Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble
Anaram Yaghoobi Notash1,2, Aidin Yaghoobi Notash2, Zahra Omidi3
1The Computer Engineering Department, Rasht Branch, Islamic Azad University, Rasht, Iran.
Background:
Breast cancer-related lymphedema is one of the most important complications that adversely affect patients' quality of life. Lymphedema can be managed if its risk factors are known and can be modified. This study aimed to select an appropriate model to predict the risk of lymphedema and determine the factors affecting lymphedema.
Method:
This study was conducted on data of 970 breast cancer patients with lymphedema referred to a lymphedema clinic. This study was designed in two phases: developing an appropriate model to predict the risk of lymphedema and identifying the risk factors. The first phase included data preprocessing, optimizing feature selection for each base learner by the Genetic algorithm, optimizing the combined ensemble learning method, and estimating fitness function for evaluating an appropriate model. In the second phase, the influential variables were assessed and introduced based on the average number of variables in the output of the proposed algorithm.
Result:
Once the sensitivity and accuracy of the algorithms were evaluated and compared, the Support Vector Machine algorithm showed the highest sensitivity and was found to be the superior model for predicting lymphedema. Meanwhile, the combined method had an accuracy coefficient of 91%. The extracted significant features in the proposed model were the number of lymph nodes to the number of removed lymph nodes ratio (68%), feeling of heaviness (67%), limited range of motion in the affected limb (65%), the number of the removed lymph nodes ( 64%), receiving radiotherapy (63%), misalignment of the dominant and the involved limb (62%), presence of fibrotic tissue (62%), type of surgery (62%), tingling sensation (62%), the number of the involved lymph nodes (61%), body mass index (61%), the number of chemotherapy sessions (60%), age (58%), limb injury (53%), chemotherapy regimen (53%), and occupation (50%).
Conclusion:
Applying a combination of ensemble learning approach with the selected classification algorithms, feature selection, and optimization by Genetic algorithm, Lymphedema can be predicted with appropriate accuracy. Developing applications by effective variables to determine the risk of lymphedema can help lymphedema clinics choose the proper preventive and therapeutic method.
