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

S. Dehdar; K. Salimifard; R. Mohammadi; M. Marzban; S. Saadatmand; M. Fararouei; M. Dianati-Nasab

doi:10.3389/fonc.2023.1103369

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

S. Dehdar, K. Salimifard^*, R. Mohammadi, M. Marzban, S. Saadatmand, M. Fararouei, M. Dianati-Nasab^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

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.

Original language	English
Article number	1103369
Number of pages	10
Journal	Frontiers in Oncology
Volume	13
DOIs	https://doi.org/10.3389/fonc.2023.1103369
Publication status	Published - 16 Feb 2023

Keywords

PATIENT DELAY
REASONS
SELECTION
SOCIODEMOGRAPHIC FACTORS
STAGE
STATISTICS
WOMEN
breast cancer (BC)
delay
extreme gradient boosting
logistic regression
machine learning
neural networks (NN)
random forest (RF)
Extreme gradient boosting
Delay
Logistic regression
Machine learning

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10.3389/fonc.2023.1103369Licence: CC BY

Cite this

@article{7eaaff6b008e4f6d80273f293b11ea88,

title = "Applications of different machine learning approaches in prediction of breast cancer diagnosis delay",

abstract = "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.",

keywords = "PATIENT DELAY, REASONS, SELECTION, SOCIODEMOGRAPHIC FACTORS, STAGE, STATISTICS, WOMEN, breast cancer (BC), delay, extreme gradient boosting, logistic regression, machine learning, neural networks (NN), random forest (RF), Extreme gradient boosting, Delay, Logistic regression, Machine learning",

author = "S. Dehdar and K. Salimifard and R. Mohammadi and M. Marzban and S. Saadatmand and M. Fararouei and M. Dianati-Nasab",

note = "Funding Information: We would like to thank all the participants and wish them all happiness. Publisher Copyright: Copyright {\textcopyright} 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab.",

year = "2023",

month = feb,

day = "16",

doi = "10.3389/fonc.2023.1103369",

language = "English",

volume = "13",

journal = "Frontiers in Oncology",

issn = "2234-943X",

publisher = "Frontiers Media S.A.",

}

TY - JOUR

T1 - Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

AU - Dehdar, S.

AU - Salimifard, K.

AU - Mohammadi, R.

AU - Marzban, M.

AU - Saadatmand, S.

AU - Fararouei, M.

AU - Dianati-Nasab, M.

N1 - Funding Information: We would like to thank all the participants and wish them all happiness. Publisher Copyright: Copyright © 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab.

PY - 2023/2/16

Y1 - 2023/2/16

N2 - 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.

AB - 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.

KW - PATIENT DELAY

KW - REASONS

KW - SELECTION

KW - SOCIODEMOGRAPHIC FACTORS

KW - STAGE

KW - STATISTICS

KW - WOMEN

KW - breast cancer (BC)

KW - delay

KW - extreme gradient boosting

KW - logistic regression

KW - machine learning

KW - neural networks (NN)

KW - random forest (RF)

KW - Extreme gradient boosting

KW - Delay

KW - Logistic regression

KW - Machine learning

U2 - 10.3389/fonc.2023.1103369

DO - 10.3389/fonc.2023.1103369

M3 - Article

C2 - 36874113

SN - 2234-943X

VL - 13

JO - Frontiers in Oncology

JF - Frontiers in Oncology

M1 - 1103369

ER -