References

RESEARCH ON BREAST CANCER CLASSIFICATION BASED ON PCA AND K-NEAREST NEIGHBOR


[1] J. Kim, A. Harper, V. McCormack, H. Sung, N. Houssami, E. Morgan et al., Global patterns and trends in breast cancer incidence and mortality across 185 countries, Nature Medicine 31(4) (2025), 1154-1162.
DOI: https://doi.org/10.1038/s41591-025-03502-3

[2] R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data. 3rd ed. Hoboken, NJ: Wiley, 2020.

[3] S. S. Roy, S. Mallik, F. Ferretti, et al., KNN in high-dimensional spaces: Challenges and adaptive solutions for pattern recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 45(8) (2023), 9456-9472.

[4] S. S. Roy, S. Mallik, F. Ferretti et al., Adaptive-weighted KNN for on-device hyperspectral image classification in IoT remote sensing, IEEE Transactions on Geoscience and Remote Sensing 61 (2023), 1-15.

[5] I. T. Jolliffe and J. Cadima, Principal component analysis: A review and recent developments, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065) (2016), 20150202.
DOI: https://doi.org/10.1098/rsta.2015.0202

[6] M. N. Gurcan, L. E. Boucheron, A. Can, et al., Nuclear feature extraction for breast tumor diagnosis using deep learning, Journal of Pathology Informatics 13(1) (2022), 45.

[7] D. Enders and P. K. Li, Multiple imputation: A flexible tool for handling missing data in medical research. Biostatistics 22(3) (2021), 510-525.

[8] L. Al Shalabi, Z. Shaaban and B. Kasasbeh, Data mining preprocessing: Z-score normalization for enhanced classification in imbalanced datasets, Journal of Computer Science 18(4) (2022), 289-301.

[9] Y. Zhang, L. Wang and X. Li, Nearest neighbor pattern classification in high-dimensional data: Modern adaptations and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2) (2024), 789-802.

[10] T. G. Dietterich, A study of cross-validation and bootstrap for accuracy estimation and model selection in modern ML. Journal of Machine Learning Research, 23(1) (2022), 1-45.

[11] M. Sokolova and G. Lapalme, Approximate statistical tests for comparing supervised classification learning algorithms: Recent empirical evaluations, Neural Computation. 35(7) (2023), 1895-1923.

[12] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval (2nd ed., updated 2021). Cambridge: Cambridge University Press, 2021.