Comparison of the Performance of Nearest Neighbor and Support Vector Machine Algorithms for Breast Cancer Prediction in Active Women

Document Type : Original Article(s)

Authors

1 PhD Student of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

2 Professor of Exercise Physiology, Department of Physical Education & Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

3 Associate Professor, Department of Physiology, Faculty of Medical Sciences, University of Granada, Granada, Spain

10.48305/jims.v43.i833.1213

Abstract

Background: Breast cancer is one of the most common cancers in women, and its timely diagnosis plays an important role in survival and treatment. Machine learning has the potential to predict breast cancer based on hidden features in the data. The main objective of this study was to predict breast cancer in active women using Nearest Neighbor and Support Vector Machine algorithms.
Methods: In this developmental-applied study, data were collected from the medical records of 641 breast cancer patients at Imam Khomeini Hospital and the Motamed Cancer Research Institute between 2014 and 2024, within the age range of 25 to 75 years. After initial preprocessing of the dataset, the Nearest Neighbor and Support Vector Machine algorithms were applied.
Findings: The results showed that the Support Vector Machine algorithm performed better than the Nearest Neighbor algorithm for predicting breast cancer in active women, with an accuracy of 87.2%, a precision of 86.35%, a sensitivity of 88.86%, and a specificity of 68.85%.
Conclusion: Data mining algorithms can be used to design novel systems that assist physicians in facilitating diagnostic and therapeutic processes. Combining multiple risk factors in modeling for breast cancer prediction can help in the early diagnosis of the disease.

Highlights

Leila Fasihi: Google Scholar

Hamid Agha-Alinejad: Google Scholar 

Reza Gharakhanlou:  Google Scholar

Francisco J. Amaro Gahete: Google Scholar

Keywords

Main Subjects

References

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Journal of Isfahan Medical School
Volume 43, Issue 833
1st Week, December
November and December 2026
Pages 1213-1219

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History

  • Receive Date: 01 January 2025
  • Accept Date: 05 October 2025

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