Comparative Analysis of Breast Cancer Detection using Cutting-edge Machine Learning Algorithms (MLAs)
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Recently, machine learning techniques have gained popularity for the medical diagnosis. Medical professionals use this approach to learn and detect the abnormalities of life-threatening chronic diseases. The increasing use of ML approaches may be due in part to better disease diagnosis enabled through improved symptom detection. The current study deployed different machine learning algorithms, including Decision Trees (DT), K-Nearest Neighbors (KNN), classifiers Multilayer Perceptron (MP), Support Vector Machines (SVM), and Random Forest (RF) for early predictions and symptoms of the disease. These models were capable of differentiating between benign and harmful cancer cells Benign tumours, which were non-cancerous and in most cases, non-lethal were mostly confined to the area from where they originated, however, it was observed that malignant cancer can start with abnormal cell growth in the human body. This abnormal cell growth can quickly spread to nearby tissues, which can cause infiltration of adjacent cells, resulting in a potentially fatal condition. Thereby, it was observed that Multilayer Perceptron (MLP) model provided the highest accuracy percentage of 86% when compared with all the other techniques in association with the accuracy rate of the models
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D. Lazaro-Pacheco, A. M. Shaaban, S. Rehman, and I. Rehman, “Raman spectroscopy of breast cancer,” Appl. Spectrosc. Rev., vol. 55, no. 6, pp. 439–475, 2020, doi: https://doi.org/10.1080/05704928.2019.1601105
M. Arnold et al., “Current and future burden of breast cancer: Global statistics for 2020 and 2040,” The Breast, vol. 66, pp. 15–23, 2022, doi: https://doi.org/10.1016/j.breast.2022.08.010
F. Gorunescu, “Fighting breast cancer with the aid of artificial intelligence: A big challenge,” J. Cancer Clin. Res., vol. 3, no. 1, Art. no. 1069.
N. A. Mashudi, S. A. Rossli, N. Ahmad, and N. M. Noor, “Comparison on some machine learning techniques in breast cancer classification,” in 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), IEEE, 2021, pp. 499–504, doi: https://doi.org/10.1109/IECBES48179.2021.9398837
S. Saeed, N. Z. Jhanjhi, M. Naqvi, M. Humyun, M. Ahmad, and L. Gaur, “Optimized breast cancer premature detection method with computational segmentation: A systematic review mapping,” Approaches Appl. Deep Learn. Virtual Med. Care, pp. 24–51, 2022, doi: https://doi.org/10.4018/978-1-7998-8929-8.ch002
W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu, “Machine learning with applications in breast cancer diagnosis and prognosis,” Designs, vol. 2, no. 2, Art. no. 13, May 2018, doi: https://doi.org/10.3390/designs2020013
L. Galluzzi et al., “Autophagy in malignant transformation and cancer progression,” EMBO J., vol. 34, no. 7, pp. 856–880, 2015, doi: https://doi.org/10.15252/embj.201490784
H. Asri, H. Mousannif, H. Al Moatassime, and T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis,” Procedia Comput. Sci., vol. 83, pp. 1064–1069, 2016, doi: https://doi.org/10.1016/j.procs.2016.04.224
H. Wang, B. Zheng, S. W. Yoon, and H. S. Ko, “A support vector machine-based ensemble algorithm for breast cancer diagnosis,” Eur. J. Oper. Res., vol. 267, no. 2, pp. 687–699, 2018, doi: https://doi.org/10.1016/j.ejor.2017.12.001
N. Fatima, L. Liu, S. Hong, and H. Ahmed, “Prediction of breast cancer, comparative review of machine learning techniques, and their analysis,” IEEE Access, vol. 8, pp. 150360–150376, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3016715
M. Mahmood, B. Al-Khateeb, and W. M. Alwash, “A review on neural networks approach on classifying cancers,” IAES Int. J. Artif. Intell., vol. 9, no. 2, p. 317, 2020, doi: https://doi.org/1010.11591/ijai.v9.i2.pp317-326
B. M. Gayathri and C. P. Sumathi, “Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer,” in 2016 IEEE Int. Conf. Comput. Intell. Comput. Res., IEEE, 2016, pp. 1–5, doi: https://doi.org/10.1109/ICCIC.2016.7919576
Y. Khourdifi and M. Bahaj, “Applying best machine learning algorithms for breast cancer prediction and classification,” in 2018 Int. conf. Elec. Cont. Optim. Comput. Sci., IEEE, 2018, pp. 1–5, doi: https://doi.org/10.1109/ICECOCS.2018.8610632
A. H. Osman, “An enhanced breast cancer diagnosis scheme based on two-step-SVM technique,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 4, 2017, doi: https://doi.org/10.14569/IJACSA.2017.080423
D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, “Machine learning classification techniques for breast cancer diagnosis,” in IOP Conf. Series: Mat. Sci. Eng., 2019, Art. no. 12033, doi: https://doi.org/10.1088/1757-899X/495/1/012033
M. A. Naji, S. El Filali, K. Aarika, E. L. H. Benlahmar, R. A. Abdelouhahid, and O. Debauche, “Machine learning algorithms for breast cancer prediction and diagnosis,” Procedia Comput. Sci., vol. 191, pp. 487–492, 2021, doi: https://doi.org/10.1016/j.procs.2021.07.062
V. Mikhailova and G. Anbarjafari, “Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning,” Med. Biol. Eng. Comput., vol. 60, no. 9, pp. 2589–2600, July 2022, doi: https://doi.org/10.1007/s11517-022-02623-y
S. Aruna, S. P. Rajagopalan, and L. V Nandakishore, “Knowledge based analysis of various statistical tools in detecting breast cancer,” Comput. Sci. Inf. Technol., vol. 2, no. 2011, pp. 37–45, 2011, doi: https://doi.org/10.5121/csit.2011.1205
J. L. Bernal, S. Cummins, and A. Gasparrini, “Interrupted time series regression for the evaluation of public health interventions: a tutorial,” Int. J. Epidemiol., vol. 46, no. 1, pp. 348–355, Feb. 2017, doi: https://doi.org/10.1093/ije/dyw098
R. Nithya and B. Santhi, “Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer,” Int. J. Comput. Appl., vol. 28, no. 6, pp. 21–25, Aug. 2011.
K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” Comput. Struct. Biotechnol. J., vol. 13, pp. 8–17, 2015, doi: https://doi.org/10.1016/j.csbj.2014.11.005
D. Oyewola, D. Hakimi, K. Adeboye, and M. D. Shehu, “Using five machine learning for breast cancer biopsy predictions based on mammographic diagnosis,” Int. J. Eng. Technol. IJET, vol. 2, no. 4, pp. 142–145, 2016, doi: https://doi.org/10.19072/ijet.280563
M. K. Hasan, M. M. Islam, and M. M. A. Hashem, “Mathematical model development to detect breast cancer using multigene genetic programming,” in 2016 5th Int. Conf. Info. Elect. Vision, IEEE, 2016, pp. 574–579, doi: https://doi.org/10.1109/ICIEV.2016.7760068
E. Venkatesan and T. Velmurugan, “Performance analysis of decision tree algorithms for breast cancer classification,” Indian J. Sci. Technol., vol. 8, no. 29, pp. 1–8, Nov. 2015, doi: https://doi.org/10.17485/ijst/2015/v8i1/84646
T. S. Rana, H. M. Usman, and S. Naseer, “Static handwritten signature verification using convolution neural network,” in 2019 Int. Conf. Inn. Comput., IEEE, 2019, pp. 1–6, doi: https://doi.org/10.1109/ICIC48496.2019.8966696
T. S. Rana and A. Ashraf, “Bladder and kidney cancer genome classification using neural network,” VFAST Transac. Software Eng., vol. 9, no. 2, June 2021.
D. Lazaro-Pacheco, A. M. Shaaban, S. Rehman, and I. Rehman, “Raman spectroscopy of breast cancer,” Appl. Spectrosc. Rev., vol. 55, no. 6, pp. 439–475, 2020, doi: https://doi.org/10.1080/05704928.2019.1601105
E. U. Khadim, S. A. Shah and R. A. Wagan, "Evaluation of Activation Functions in CNN Model for Detection of Malaria Parasite using Blood Smear Images," 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan, 2021, pp. 1-6, doi: 10.1109/ICIC53490.2021.9693056
I. Ahmad et al., "Emerging Technologies for Next Generation Remote Health Care and Assisted Living," in IEEE Access, vol. 10, pp. 56094-56132, 2022, doi: 10.1109/ACCESS.2022.3177278
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