Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Authors

  • Rebecca Nyasuguta Arika School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • Agnes Mindila School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • W. Cheruiyo School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

DOI:

https://doi.org/10.30564/jor.v5i1.4977

Abstract

Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.

Keywords:

Algorithm; Quantum computing; Machine learning; Breast cancer; Prediction

References

[1] Al-Azzam, N., Shatnawi, I., 2021. Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer. Annals of Medicine and Surgery. 62, 53-64.

[2] Rahman, M.M., Ghasemi, Y., Suley, E., et al., 2021. Machine learning based computer aided diagnosis of breast cancer utilizing anthropometric and clinical features. Irbm. 42(4), 215-226.

[3] Ara, S., Das, A., Dey, A., 2021, April. Malignant and benign breast cancer classification using machine learning algorithms. 2021 International Conference on Artificial Intelligence (ICAI). pp. 97-101. IEEE.

[4] Thalor, A., Joon, H.K., Singh, G., et al., 2022. Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer. Computational and Structural Biotechnology Journal. 20, 1618-1631.

[5] Safdar, S., Rizwan, M., Gadekallu, T.R., et al., 2022. Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection. Diagnostics. 12(5), 1134.

[6] Nyangaresi, V.O., El-Omari, N.K.T., Nyakina, J.N., 2022. Efficient Feature Selection and ML Algorithm for Accurate Diagnostics. Journal of Computer Science Research. 4(1), 10-19.

[7] Chugh, G., Kumar, S., Singh, N., 2021. Survey on machine learning and deep learning applications in breast cancer diagnosis. Cognitive Computation. 13(6), 1451-1470.

[8] Rane, N., Sunny, J., Kanade, R., et al., 2020. Breast cancer classification and prediction using machine learning. International Journal of Engineering Research and Technology. 9(2), 576-580.

[9] Alaa, A.M., Gurdasani, D., Harris, A.L., et al., 2021. Machine learning to guide the use of adjuvant therapies for breast cancer. Nature Machine Intelligence. 3(8), 716-726.

[10] Mohammed, S.A., Darrab, S., Noaman, S.A., et al., 2020. Analysis of breast cancer detection using different machine learning techniques. International Conference on Data Mining and Big Data. pp. 108- 117. Springer, Singapore.

[11] Shravya, C., Pravalika, K., Subhani, S., 2019. Prediction of breast cancer using supervised machine learning techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 8(6), 1106-1110.

[12] Jasti, V., Zamani, A.S., Arumugam, K., et al., 2022. Computational technique based on machine learningand image processing for medical image analysis of breast cancer diagnosis. Security and Communication Networks.

[13] Srinivas, T., Karigiri Madhusudhan, A.K., Arockia Dhanraj, J., et al., 2022. Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer. Mathematical Problems in Engineering.

[14] Rasool, A., Bunterngchit, C., Tiejian, L., et al., 2022. Improved machine learning-based predictive models for breast cancer diagnosis. International Journal of Environmental Research and Public Health. 19(6), 3211.

[15] Ahuja, A., Al-Zogbi, L., Krieger, A., 2021. Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification. Computers in Biology and Medicine. 135, 104576.

[16] Binder, A., Bockmayr, M., Hägele, M., et al., 2021. Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence. 3(4), 355-366.

[17] Sung, H., Ferlay, J., Siegel, R.L., et al., 2021. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer Journal for Clinicians. 71(3), 209-249.

[18] Tariq Jamal, A., Ben Ishak, A., Abdel-Khalek, S., 2021. Tumor edge detection in mammography images using quantum and machine learning approaches. Neural Computing and Applications. 33(13), 7773- 7784.

[19] Chaudhury, S., Krishna, A.N., Gupta, S., et al., 2022. Effective image processing and segmentation-based machine learning techniques for diagnosis of breast cancer. Computational and Mathematical Methods in Medicine.

[20] Nyangaresi, V.O., Rodrigues, A.J., Abeka, S.O., 2020, December. ANN-FL secure handover protocol for 5G and beyond networks. International Conference on e-Infrastructure and e-Services for Developing Countries. pp. 99-118. Springer, Cham.

[21] Zhang, Y., Ni, Q., 2020. Recent advances in quantum machine learning. Quantum Engineering. 2(1), e34.

[22] Nyangaresi, V.O., Ahmad, M., Alkhayyat, A., et al., 2022. Artificial neural network and symmetric key cryptography based verification protocol for 5G enabled Internet of Things. Expert Systems. pp. e13126.

[23] Houssein, E.H., Emam, M.M., Ali, A.A., et al., 2021. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications. 167, 114161.

[24] Moloney, B.M., O’Loughlin, D., Abd Elwahab, S., et al., 2020. Breast cancer detection—A synopsis of conventional modalities and the potential role of microwave imaging. Diagnostics. 10(2), 103.

[25] Sharma, U., Jagannathan, N.R., 2022. Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism. Metabolites. 12(4), 295.

[26] Haque, M.N., Tazin, T., Khan, M.M., et al., 2022. Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches. Computational and Mathematical Methods in Medicine.

[27] Zerouaoui, H., Idri, A., 2021. Reviewing machine learning and image processing based decision-making systems for breast cancer imaging. Journal of Medical Systems. 45(1), 1-20.

[28] Yu, Y., He, Z., Ouyang, J., et al., 2021. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine. 69, 103460.

[29] Nyangaresi, V.O., Rodrigues, A.J., 2022. Efficient handover protocol for 5G and beyond networks. Computers & Security. 113, 102546.

[30] Alzu’bi, A., Najadat, H., Doulat, W., et al., 2021. Predicting the recurrence of breast cancer using machine learning algorithms. Multimedia Tools and Applications. 80(9), 13787-13800.

[31] Li, J., Zhou, Z., Dong, J., et al., 2021. Predicting breast cancer 5-year survival using machine learning: A systematic review. PloS one. 16(4), e0250370.

[32] Mirsadeghi, L., Haji Hosseini, R., Banaei-Moghaddam, A.M., et al., 2021. EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer. BMC Medical Genomics. 14(1), 1-19.

[33] Meenalochini, G., Ramkumar, S., 2021. Survey of machine learning algorithms for breast cancer detection using mammogram images. Materials Today: Proceedings. 37, 2738-2743.

[34] Michael, E., Ma, H., Li, H., et al., 2022. An optimized framework for breast cancer classification using machine learning. BioMed Research International.

[35] Kumar, V., Mishra, B.K., Mazzara, M., et al., 2020. Prediction of malignant and benign breast cancer: A data mining approach in healthcare applications. Advances in Data Science and Management. pp. 435-442. Springer, Singapore.

[36] Al Sibahee, M.A., Ma, J., Nyangaresi, V.O., et al., 2022, June. Efficient Extreme Gradient Boosting Based Algorithm for QoS Optimization in Inter-Radio Access Technology Handoffs. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). pp. 1-6. IEEE.

[37] Haq, A.U., Li, J.P., Saboor, A., et al., 2021. Detection of breast cancer through clinical data using supervised and unsupervised feature selection techniques. IEEE Access. 9, 22090-22105.

[38] Vy, V.P.T., Yao, M.M.S., Khanh Le, N.Q., et al., 2022. Machine Learning Algorithm for Distinguishing Ductal Carcinoma in Situ from Invasive Breast Cancer. Cancers. 14(10), 2437.

[39] Karatza, P., Dalakleidi, K., Athanasiou, M., et al., 2021, November. Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). pp. 2310-2313. IEEE.

[40] Nyangaresi, V.O., Rodrigues, A.J., Abeka, S.O., 2022. Machine Learning Protocol for Secure 5G Handovers. International Journal of Wireless Information Networks. 29(1), 14-35.

[41] Sammut, S.J., Crispin-Ortuzar, M., Chin, S.F., et al., 2022. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 601(7894), 623-629.

[42] Nyangaresi, V.O., Rodrigues, A.J., Abeka, S.O., 2020, October. Neuro-fuzzy based handover authentication protocol for ultra dense 5G networks. 2020 2nd Global Power, Energy and Communication Conference (GPECOM). pp. 339-344. IEEE.

[43] Khan, T.M., Robles-Kelly, A., 2020. Machine learning: Quantum vs classical. IEEE Access. 8, 219275- 219294.

[44] Azevedo, V., Silva, C., Dutra, I., 2022. Quantum transfer learning for breast cancer detection. Quantum Machine Intelligence. 4(1), 1-14.

[45] Salehi, T., Zomorodi, M., Plawiak, P., et al., 2022. An optimizing method for performance and resource utilization in quantum machine learning circuits. Scientific Reports. 12(1), 1-16.

[46] Gupta, H., Varshney, H., Sharma, T.K., et al., 2022. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex & Intelligent Systems. 8(4), 3073- 3087.

[47] Nyangaresi, V.O., Rodrigues, A.J., Abeka, S.O., 2020. Secure Handover Protocol for High Speed 5GNetworks. International Journal of Advanced Networking and Applications. 11(06), 4429-4442.

[48] Li, W., Lu, S., Deng, D.L., 2021. Quantum federated learning through blind quantum computing. Science China Physics, Mechanics & Astronomy. 64(10), 1-8.

[49] Sharifi, M., Hasan, A., Attar, F., et al., 2020. Development of point-of-care nanobiosensors for breast cancers diagnosis. Talanta. 217, 121091.

[50] Martina, S., Buffoni, L., Gherardini, S., et al., 2022. Learning the noise fingerprint of quantum devices. Quantum Machine Intelligence. 4(1), 1-12.

[51] Nyangaresi, V.O., Abeka, S.O., Rodgrigues, A., 2018. Secure Timing Advance Based Context-Aware Handover Protocol for Vehicular Ad-Hoc Heterogeneous Networks. International Journal of Cyber-Security and Digital Forensics. 7(3), 256-275.

[52] Li, R.Y., Gujja, S., Bajaj, S.R., et al., 2021. Quantum processor-inspired machine learning in the biomedical sciences. Patterns. 2(6), 100246.

[53] Pomarico, D., Fanizzi, A., Amoroso, N., et al., 2021. A proposal of quantum-inspired machine learning for medical purposes: An application case. Mathematics. 9(4), 410.

[54] Nyangaresi, V.O., Abeka, S.O., Rodrigues, A.J., 2020. Delay Sensitive Protocol for High Availability LTE Handovers. American Journal of Networks and Communications. 9(1), 1-10.

[55] Yala, A., Mikhael, P.G., Strand, F., et al., 2022. Multi-institutional validation of a mammography-based breast cancer risk model. Journal of Clinical Oncology. 40(16), 1732-1740.

[56] Chakravarthy, S.S., Rajaguru, H., 2022. Automatic detection and classification of mammograms using improved extreme learning machine with deep learning. IRBM. 43(1), 49-61.

Downloads

How to Cite

Arika, R. N., Mindila, A., & Cheruiyo, W. (2023). Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions. Journal of Oncology Research, 5(1). https://doi.org/10.30564/jor.v5i1.4977

Issue

Vol. 5 , Iss. 1 (January 2023)

Article Type

Articles

License

Copyright and Licensing

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

Journal of Oncology Research publishes accepted manuscripts under Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Authors who submit their papers for publication by Journal of Oncology Research agree to have the CC BY-NC 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for non-commercial use. As long as you follow the license terms and original source is properly cited, anyone may copy, redistribute the material in any medium or format, remix, transform, and build upon the material.

License Policy for Reuse of Third-Party Materials

If a manuscript submitted to the journal contains the materials which are held in copyright by a third-party, authors are responsible for obtaining permissions from the copyright holder to reuse or republish any previously published figures, illustrations, charts, tables, photographs, and text excerpts, etc. When submitting a manuscript, official written proof of permission must be provided and clearly stated in the cover letter.
The editorial office of the journal has the right to reject/retract articles that reuse third-party materials without permission.

Journal Policies on Data Sharing

We encourage authors to share articles published in our journal to other data platforms, but only if it is noted that it has been published in this journal.