Machine Learning Techniques for Breast Cancer Prediction A Concise Review
Article Sidebar
Main Article Content
Abstract
Breast cancer is the most trending type of cancer globally with close to two and half million cases recorded based on research by the World Health Organization in 2021 and it is also the most common cancer among women in all countries, posing a major cause for public health concern. In Nigeria and the world at large, over a hundred thousand new cases of cancer occur every year with high death among women. It has been researched that early and accurate detection of breast cancer can aid in the diagnosis of the disease for women and it may also reduce the risk of death rate among women. Literature shows that several machine learning techniques have been carried out on breast cancer diagnosis to help provide accurate technology solutions to early detection. The machine learning techniques used have different accuracy rate which varies for dissimilarity conditions. In this study, we compared different methods with many existing machine-learning techniques commonly used for breast cancer detection and diagnosis. Also, the aim of this review method will show an improvement in accuracy performances by implementing different methods and analysis in existing machine learning techniques to proficiently assist doctors in decision-making on an accurate detection and diagnosis of breast cancer and classifying tumors as benign or malignant thereby reducing the risk of death rate among women.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors hold the copyright of all published articles except otherwise stated.
References
Abien Fred (2018). On Breast Cancer Detection. An Application of Machine Learning Algorithms on the wisconsin Diagnostic Dataset. International Conference on Advanced Machine Learning and Soft Computing.
Akram, M., Iqbal. M.,& Daniyal. M., & Kha, A.U(2017). Awareness and current knowledge of breast cancer. Biological Research 50(33).
Ali, E., & Feng, W. (2013). Breast Cancer classification using Support Vector Machine and Neural Network: International Journal of Science and Research,23:19-7064. Aminikhanghahi S., Shin S., Wang W., Jeon S., Son S., and Pack C., Study of wireless mammography image transmission impactson robust cyber-aided diagnosis systems: Proc. 30th Annu. ACM Symp. Appl. Comput. - SAC ’15: 2252–2256, 2015.
Andre, R., &Rangayyan, M.(2006). Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features, J. Electron. Imaging 15(1) 13019.
Avramov, T.,&Si, D. (2017). Comparison of Feature Reduction Methods and Machine Learning Models for Breast Cancer Diagnosis. Proc. Int. Conf. Comput. Data Anal. – ICCDAL.17, 69–74.
Ayeldeen, H., Elfattah, M., Shaker, O., Hassanien, A., & Kim, T. (2015). Case-Based Retrieval Approach of Clinical Breast Cancer Patients. 2015 3rd International Conference of Computer. Information and. Application, 38–41. Azar, A., &El-Said, S. (2014). Performance analysis of support vector machines classifiers in breast cancer mammography recognition, Neural Comput. Appl.24 (5):1163–1177.
Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo,M., &Moschetta, M. (2016). An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification.
In Proceeding 2016 Genetic Evolution of Computer Conference Companion - GECCO ’16 Companion, 1385–1392. Caplan, L. (2014). Delay in breast cancer: implications for the stage at diagnosis and survival. Frontiers in
Public Health, 2(87)1–6.
Bojana, R.,&Andjelkolvic, C.(2020). Machine Learning Approach for Breast Cancer Prognosis Prediction. Computational Modeling in Bioengineering and Bioinformatics, 41-68. Cesnik, V.,Vieira, E., Giami, A., Almeida, A., Santos, D., &Santos, M. (2013). The sexual life of women with breast cancer: meanings attributed to the diagnosis and its impact on sexuality. EstudPsicol, 30(2)187–197.
Chowdhary, C., &Acharjya, D. (2016). Breast Cancer Detection using Intuitionistic Fuzzy Histogram Hyperbolization and Possibility Fuzzy c-mean Clustering algorithms with texture featurebased Classification on Mammography Images. In Proceedings of the International Conference on Advances in Information Communication Technology and Computing, Bikaner, India,1–6.
Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine, 34(2) 113-127.
Duda, R., Hart, P.,&Stork, D. (2000). Text of Dimensionality Reduction, Pattern classification, 2nd edition, Wiley-Inter science. ISBN 0-471- 05669-3, SECTION (8)79/679.
Hamsa, B.,&Wichinpong, P.(2021). Improving Human Decision-Making with Machine Learning University of Pennsylvanial. Berkeley, 4(2)1-2.
Harry Zhang. (2005). Machine Learning and Neural Network Approaches to Feature Selection and Extraction for Classification. Exploring Conditions for the Optimality of Naïve Bay. International Journal of Pattern Recognition and Artificial Intelligence, 19(2) 183-198.
Mohamed, A., Jumeily, O.,Jumeily, O., Ahmed, J., & Aljaaf A., J. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science Computer Science Department. Conway, Arkansas, US, 1-30.
Mounita, G., Mohsin, S., Laboni, A.&Alshamrani, M.(2021). A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease. Department of Computer Science, College of Computers and Information Technology Intelligent Automation & Soft Computing, 30(3) 918-928.
Murugan, S., Muthu, B.,&Amudha, S. (2017). Classification and Prediction of Breast Cancer using Linear Regression, Decision Tree and Random Forest. International Conference on Current Trends in Computer, Electrical. Electronics and Communication (CTCEEC) 1-25.
Kaminskal. M., Ciszewski, T., Łopacka-Szatan, K., Miotłal. P.,&Starosławskal. E. (2015). Breast cancer risk factors. PrzegladMenopauzalny Menopause Review, 14(3)196, Witten, I., &Frank, E. (2006). Data mining: practical machine learning tools and techniques. BioMedical Engineering on Line. Computer Science Research Institute, University of Ulster, Jordanstown, Co. Antrim, BT37 0QB, Northern Ireland, UK, 1-2.
Seo, L., Paulina, M., Ryan, P., and James, J. (2020). Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS. Development and Application of Statistical methods for Analyzing Metablolomicsdata. Department of Chemistry, University of Albertal. Edmonton, AB T6G 2G2, Canada. 10(9) 376.