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

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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
    Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
    School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan
    School of Medicine, Taipei Medical University, Taipei 11031, Taiwan)

  • Yun-Xuan Tang

    (Department of Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
    Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu 30015, 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
    Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 24205, Taiwan
    Department of Information Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan)

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.

Suggested Citation

  • Cheuk-Kay Sun & Yun-Xuan Tang & Tzu-Chi Liu & Chi-Jie Lu, 2022. "An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors," IJERPH, MDPI, vol. 19(15), pages 1-17, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9756-:d:882899
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    References listed on IDEAS

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    1. Khishigsuren Davagdorj & Van Huy Pham & Nipon Theera-Umpon & Keun Ho Ryu, 2020. "XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction," IJERPH, MDPI, vol. 17(18), pages 1-22, September.
    2. John Tomkinson, 2019. "Age at first birth and subsequent fertility: The case of adolescent mothers in France and England and Wales," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(27), pages 761-798.
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    Cited by:

    1. Yung-Chuan Huang & Yu-Chen Cheng & Mao-Jhen Jhou & Mingchih Chen & Chi-Jie Lu, 2023. "Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran," IJERPH, MDPI, vol. 20(3), pages 1-15, January.

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