October 4, 2023
Khodakaram Salimifard

Khodakaram Salimifard

Academic Rank: Associate professor
Degree: Ph.D in Operations Research
Phone: 07731222118
Faculty: School of Business and Economics


Title Applications of different machine learning approaches in prediction of breast cancer diagnosis delay
Type Article
breast cancer (BC), random forest (RF), neural networks (NN), delay, machine learning, extreme gradient boosting, logistic regression
Journal Frontiers in Oncology
DOI https://doi.org/10.3389/fonc.2023.1103369
Researchers Samira Dehdar (First researcher) , Khodakaram Salimifard (Second researcher) , Reza Mohammadi (Third researcher) , Maryam Marzban (Fourth researcher) , Sara Saadatmand (Fifth researcher) , Mohammad Fararouei (Not in first six researchers) , Mostafa Dianati-Nasab (Not in first six researchers)


Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in di