Evaluation of Missing Data Imputation Methods and PCA Techniques for Machine Learning Models in Breast Cancer Diagnosis Using WBCD

Abstract

Cancer is one of the leading causes of human mortality and breast cancer deaths are particularly common among women. Early diagnosis of breast cancer is considered a key way to reduce these deaths. The use of expert systems, artificial intelligence and machine learning techniques in the medical field aims to assist doctors in early disease detection. One of the main objectives of these technologies is to diagnose life-threatening diseases such as breast cancer earlier and more accurately. In this study, analyses conducted on the Wisconsin Breast Cancer Dataset (WBCD) evaluated the effects of different missing data imputation methods and PCA-based data reduction technique on model performance using supervised machine learning methods. In the first stage of the study, the detection and management of missing values in the dataset were addressed. It was found that imputing missing values with median performed better compared to other methods. Subsequently, the dataset was reduced in size using the PCA method and the performance of algorithms was measured by experimenting with different numbers of components. The results indicate that effectively addressing the missing data problem and using PCA-based data reduction techniques significantly improve model performance. The best performance was achieved by imputing missing data with median values and reducing data dimensionality with PCA. This study emphasizes the importance of combining machine learning approaches for breast cancer diagnosis with missing data management strategies. Additionally, the effects of different missing data imputation methods and PCA on model performance have been thoroughly examined.

Keywords

• Breast cancer
• Machine learning
• Missing data management
• PCA
• Biomedical
• Signal Processing

References

1. Sun YS, Zhao Z, Yang ZN, Xu F, Lu HJ, Zhu ZY, et al. Risk factors and preventions of breast cancer. Int J Biol Sci 2017;13:1387–97. https://doi.org/10.7150/ijbs.21635.
2. Cardoso F, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rubio IT, et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology 2019;30:1194–220. https://doi.org/10.1093/annonc/mdz173.
3. Ginsburg O, Yip CH, Brooks A, Cabanes A, Caleffi M, Yataco JAD, et al. Breast Cancer Early Detection: A Phased Approach to Implementation. Cancer 2020;126:2379–93. https://doi.org/10.1002/cncr.32887.
4. Global Breast Cancer Initiative Implementation Framework Assessing, strengthening and scaling up services for the early detection and management of breast cancer. Geneva: 2023.
5. Ting Sim JZ, Fong QW, Huang W, Tan CH. Machine learning in medicine: what clinicians should know. Singapore Med J 2023;64:91–7. https://doi.org/10.11622/smedj.2021054.
6. Saturi S. Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis. Regen Eng Transl Med 2023;9:141–64. https://doi.org/10.1007/s40883-022-00273-y.
7. Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc 2023;16:1779–91. https://doi.org/10.2147/JMDH.S410301.
8. Savić M, Kurbalija V, Ilić M, Ivanović M, Jakovetić D, Valachis A, et al. The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients. Computer Science and Information Systems 2023;29:381–404. https://doi.org/10.2298/CSIS220227061S.

9. Hasan MM, Haque MR, Kabir MMJ. Breast Cancer Diagnosis Models Using PCA and Different Neural Network Architectures. 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), 2019, p. 1–4. https://doi.org/10.1109/IC4ME247184.2019.9036627.
10. Mushtaq Z, Yaqub A, Hassan A, Su SF. Performance Analysis of Supervised Classifiers Using PCA Based Techniques on Breast Cancer. 2019 International Conference on Engineering and Emerging Technologies (ICEET), 2019, p. 1–6. https://doi.org/10.1109/CEET1.2019.8711868.
11. Kong D. Research on Prediction of Breast Cancer Type using Machine Learning. vol. 2023. 2023.
12. Laghmati S, Cherradi B, Tmiri A, Daanouni O, Hamida S. Classification of Patients with Breast Cancer using Neighbourhood Component Analysis and Supervised Machine Learning Techniques. 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet), 2020, p. 1–6. https://doi.org/10.1109/CommNet49926.2020.9199633.
13. Sindhuja M, Poonkuzhali S, Vigneshwaran P. Breast Cancer Classification Model Using Principal Component Analysis and Deep Neural Network. Smart Innovation, Systems and Technologies, vol. 324, Springer Science and Business Media Deutschland GmbH; 2023, p. 137–49. https://doi.org/10.1007/978-981-19-7447-2_13.
14. Parman NH, Hassan R, Zakaria NH. Breast Cancer Prediction Using Support Vector Machine Ensemble with PCA Feature Selection Method. International Journal of Innovative Computing 2024;14:15–9. https://doi.org/10.11113/ijic.v14n1.461.
15. Rani P, Kumar R, Jain A, Lamba R, Sachdeva RK, Choudhury T. PCA-DNN: A Novel Deep Neural Network Oriented System for Breast Cancer Classification. EAI Endorsed Trans Pervasive Health Technol 2023;9. https://doi.org/10.4108/eetpht.9.3533.
16. Lou J. Prediction of breast cancer based on RF, SVM and PCA. J Phys Conf Ser, vol. 2646, Institute of Physics; 2023. https://doi.org/10.1088/1742-6596/2646/1/012036.
17. Bista C, M A, Slimanzay S, Sheikh MS, Srinivasa Rao P. Breast Cancer Prediction System Utilizing Machine Learning Algorithms, Institute of Electrical and Electronics Engineers (IEEE); 2024, p. 80–4. https://doi.org/10.1109/ieeeconf61558.2024.10585589.
18. Eladallaoui HE, Elfazziki A, Sadgal M. A CNN and PCA Approach for Earlier Breast Cancer Diagnosis. Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023, Institute of Electrical and Electronics Engineers Inc.; 2023, p. 247–52. https://doi.org/10.1109/SITIS61268.2023.00045.
19. Yadav RK, Singh P, Kashtriya P. Diagnosis of Breast Cancer using Machine Learning Techniques -A Survey. Procedia Comput Sci 2023;218:1434–43. https://doi.org/10.1016/j.procs.2023.01.122.
20. Bharadiya JP. A Tutorial on Principal Component Analysis for Dimensionality Reduction in Machine Learning. Int J Innov Res Sci Eng Technol 2023;8. https://doi.org/10.5281/zenodo.8002436.
21. Wolberg Wi. Breast Cancer Wisconsin (Original) 1992.
22. Kaya S, Yağanoğlu M. An Example of Performance Comparison of Supervised Machine Learning Algorithms Before and After PCA and LDA Application: Breast Cancer Detection. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, p. 1–6. https://doi.org/10.1109/ASYU50717.2020.9259883
23. Narasimhaiah P, Nagaraju C. Machine Learning Technique for Prediction of Breast Cancer. International Journal on Recent and Innovation Trends in Computing and Communication 2023;11:368–80. https://doi.org/10.17762/ijritcc.v11i7s.7012.
24. Baneriee C, Paul S, Ghoshal M. A Comparative Study of Different Ensemble Learning Techniques Using Wisconsin Breast Cancer Dataset. 2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE), 2017, p. 1–6. https://doi.org/10.1109/ICCECE.2017.8526215.
25. Kadhim RR, Kamil MY. Comparison of breast cancer classification models on Wisconsin dataset. International Journal of Reconfigurable and Embedded Systems 2022;11:166–74. https://doi.org/10.11591/ijres.v11.i2.pp166-174.
26. Asharma S, Shinde S, Choudhary A, Raj U, Srivastava P. Machine Learning-Based Breast Cancer Detection. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024, Institute of Electrical and Electronics Engineers Inc.; 2024. https://doi.org/10.1109/ICRITO61523.2024.10522209.
27. Ahmed SS, Srivastava Y, Khan MohdG. Prediction and Diagnosis of Breast Cancer Using Machine Learning Algorithms. Asian Journal of Research in Medical and Pharmaceutical Sciences 2024;13:54–60. https://doi.org/10.9734/ajrimps/2024/v13i3261.