• Sunday Samuel Olofintuyi
Keywords: Breast cancer, Machine Learning Algorithm, Detection


One of the most widespread diseases among women today is breast cancer. Early and accurate diagnosis is key in rehabilitation and treatment. The usage of mammograms has some uncertainties in the detection rate. To develop tools for physicians for effective and early detection and diagnosis, machine learning techniques can be adopted. The introduction of Machine Learning (ML) in developing the tool will increase the survival rate of patients with breast cancer. This research work proposed different six ML techniques; Logistic Regression, Linear Discriminant Analysis, Decision Tree (DT), KNN, Naïve Bayes (NB), and Support Vector Machine (SVM), and then recommended the model with the highest accuracy for breast cancer detection. The experiment was carried out in a python environment and all the aforementioned techniques were validated with Wisconsin Breast Cancer dataset and evaluated with accuracy, precision, and recall.


Adebiyi, M.O.; Arowolo, M.O.; Mshelia, M.D.; Olugbara, O.O. (2022) A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis. Appl. Sci. 2022, 12, 11455. app122211455

Asri, H., Mousannif, H., Al, M.H., Noel, T.(2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064–1069

Chaurasia, V., Pal, S. (2017). A novel approach for breast cancer detection using data mining techniques. Int. J. Innovative Res. Comput. Commun. Eng. 2 (An ISO 3297: 2007 Certified Organization)

Dana Bazazeh and Raed Shubair (2016). Comparative Study of Machine Learning Algorithms for Breast Cancer Detection and Diagnosis. 978-1-5090-5306-3/16/$31.00 c 2016 IEEE

Hazra, A., Mandal, S.K., Gupta, A. (2016) Study and analysis of breast cancer cell detection using Naïve Bayes, SVM and Ensemble Algorithms. Int. J. Comput. Appl. 145, 0975–8887

Mohammed, S.A., Darrab, S., Noaman, S.A., Saake, G. (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan, Y., Shi, Y., Tuba, M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore.

Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, and Ayman Nabil (2021). Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark. Hindawi Complexity Vol. 2021, Article ID 6653508

Olofintuyi S.S; Olajubu E.A; Olanike D. (2023). An ensemble deep learning approach for predicting cocoa yield. Heliyon. 2023 Apr 5;9(4):e15245. Doi: 10.1016/j.heliyon.2023.e15245.

Olofintuyi, S.S. (2021). Cyber Situation Awareness Perception Model for Computer Network. International journal of advanced computer science and application. 12(1), pp. 392-397.

Olofintuyi S.S and Olajubu E.A (2021). Supervised Machine Learning Algorithms for Cyber-Threats Detection in the Perception Phase of a Situation Awareness Model. International Journal of Information Processing and Communication (IJIPC) Vol. 11 No. 2 [December, 2021], pp. 61-74

Olofintuyi, S.S., Omotehinwa, T. O., Odukoya, O.H. and Olajubu, E. A. (2019). Performance comparison of threat classification models for cyber-situation awareness. Proceedings of the OAU Faculty of Technology Conference, 305-309.

O. S. Samuel (2022). “Early Cocoa Blackpod Pathogen Prediction with Machine Learning Ensemble Algorithm based on Climatic Parameters”, J. inf. organ. sci. (Online), vol. 46, no. 1, .

Ojha U., Goel, S. (2017). A study on prediction of breast cancer recurrence using data mining techniques. In: 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, IEEE, pp. 527–530

Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S. (2016) Predicting breast cancer recurrence using effective classification and feature selection technique. In: 19th International Conference on Computer and Information Technology (ICCIT), pp. 310–314. IEEE

Rodrigues, B.L. (2015). Analysis of the Wisconsin Breast Cancer dataset and machine learning for breast cancer detection. In: Proceedings of XI Workshop de Visão Computational, pp. 15–19 (2015)

Saabith, A.L.S., Sundararajan, E., Bakar, A.A.(2014): Comparative study on different classification techniques for breast cancer dataset. Int. J. Comput. Sc. Mob. Comput. 3(10), 185–191

Siham A. Mohammed, Sadeq Darrab , Salah A. Noaman, and Gunter Saake (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. DMBD CCIS 1234, pp. 108–117,

Silva, J., Lezama, O.B.P., Varela, N., Borrero, L.A. (2019). Integration of data mining classification techniques and ensemble learning for predicting the type of breast cancer recurrence. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds.) GPC 2019. LNCS, vol. 11484, pp. 18–30. Springer, Cham (2019).

W. Lim, S. Hamid, and M. Grivna (2016). Breast cancer presentation delays among Arab and national women in the UAE, a qualitative study, SSM - Popul. Heal., Mar. 2016

WHO — Breast Cancer: Prevention and Control (2020) Retrieved 20 Jan 2023, from WHO — World Health Organization. http://

How to Cite