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Breast cancer prediction using different machine learning methods applying multi factors

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Abstract

Objective

Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.

Methods

The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.

Result

Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54).

Conclusion

Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

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Funding

This study was funded by Mashhad University of Medical Sciences (grant number 960336, 960275, 960211, 951122, 940724, 961731).

Author information

Author notes

  1. Elham Nazari and Hamid Naderi have contributed equally to this work.

Authors and Affiliations

  1. Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran

    Elham Nazari, Hamid Naderi & Hamed Tabesh

  2. Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Mahla Tabadkani & Majid Khazaei

  3. Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

    Elham Nazari, Mahla Tabadkani, Majid Khazaei & Amir Avan

  4. Halal Research Center of IRI, FDA, Tehran, Iran

    Reza ArefNezhad

  5. Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

    Reza ArefNezhad

  6. Department of Computer Engineering, Ferdowsi University, Mashhad, Iran

    Amir Hossein Farzin

  7. School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

    Mohammad Dashtiahangar

  8. Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK

    Gordon A. Ferns

  9. Warwick Medical School, University of Warwick, Coventry, UK

    Amin Mehrabian

  10. Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia

    Amir Avan

  11. College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq

    Amir Avan

  12. Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Elham Nazari

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  1. Elham Nazari
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Contributions

Study conception and design: EN, MT, AA, MK, GAF, HT Acquisition of data: EN Analysis and interpretation of data: EN, HN, MT, RA, AHF Drafting of manuscript: EN, HN, MD, AM Critical revision: AA, GAF.

Corresponding authors

Correspondence to Hamed Tabesh or Amir Avan.

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All authors have no conflict of interest.

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All procedures performed in studies involving human participants were following the ethical standards of Mashhad University of Medical Sciences and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Nazari, E., Naderi, H., Tabadkani, M. et al. Breast cancer prediction using different machine learning methods applying multi factors. J Cancer Res Clin Oncol 149, 17133–17146 (2023). https://doi.org/10.1007/s00432-023-05388-5

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