An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

Cheuk-Kay Sun, Yun-Xuan Tang, Tzu-Chi Liu and Chi-Jie Lu ()
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Cheuk-Kay Sun: Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
Yun-Xuan Tang: Department of Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
Tzu-Chi Liu: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Chi-Jie Lu: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan

IJERPH, 2022, vol. 19, issue 15, 1-17

Abstract: This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.

Keywords: mammography; machine learning; breast cancer; national mammographic screening program; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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