Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Frontiers in Oncology

Oncology

Reza Mohammadi¹  Khodakaram Salimifard²  Sara Saadatmand²  Samira Dehdar²  Mostafa Dianati-Nasab³  Mohammad Fararouei⁴  Maryam Marzban⁵ 

[1] Business Analytics Section, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands;Computational Intelligence & Intelligent Optimization Research Group, Business and Economic School, Persian Gulf University, Bushehr, Iran;Department of Complex Genetics and Epidemiology, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands;Department of Epidemiology, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran;Department of Public Health, School of Public Health, Bushehr University of Medical Science, Bushehr, Iran;

关键词: breast cancer (BC);    random forest (RF);    neural networks (NN);    delay;    machine learning;    extreme gradient boosting;    logistic regression;   

DOI  :  10.3389/fonc.2023.1103369

 received in 2022-12-03, accepted in 2023-01-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundThe 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.ObjectivesThe present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.MethodsIn 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.ResultsThirty 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.ConclusionMachine 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 diagnosis.

【 授权许可】

Unknown   
Copyright © 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab