Detection and Diagnosis of Breast Cancer Using an Ensemble Statistical Learning Method


  • Alio Boubacar Goga Département de Mathématiques et Informatique, Faculté des Sciences et Techniques, Université Abdou Moumouni, Niamey, Niger
  • Chaibou Kadri Département de Mathématiques et Informatique, Faculté des Sciences et Techniques, Université Abdou Moumouni, Niamey, Niger
  • Ibrahim Sidi Zakari Département de Mathématiques et Informatique, Faculté des Sciences et Techniques, Université Abdou Moumouni, Niamey, Niger
  • Harouna Naroua Département de Mathématiques et Informatique, Faculté des Sciences et Techniques, Université Abdou Moumouni, Niamey, Niger


Artificial Intelligence, Breast Cancer, Ensemble Method, Statistical Learning, Triple-Stacking


Breast cancer is a malignant tumor that originates in the cells of the breast. It is  the second leading cause of women’s death, after lung cancer. Moreover, the  availability of medical data facilitates the development of related Artificial Intelligence Systems (AIS). The diagnosis (or classification) of breast cancer is a delicate task, which requires efficient and robust classifiers. However,  classical classification methods (in which  a single basic classifier  ( estimator )) are generally confronted with the “bias-variance” dilemma. This, very often, affects seriously their efficiency and robustness. In this article, to mitigate this problem, we propose a new learning model called Triple-Stacking. This technique is composed of three (3) methods of statistical learning (Logistic Regression, Voting and Stacking) and a meta-learner (Decision Stump). The proposed model outperformed the existing ones on two different databases: Breast Cancer Wisconsin Original Data Set and Breast Cancer Wisconsin Diagnostic Data Set, with accuracies of  99.57% and 99.64%, respectively.


Essam H. Houssein, Marwa M. Emam, Abdelmgeid A. Ali, Ponnuthurai Nagaratnam Suganthan. “Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review”. Expert Systems With Applications, vol. 167, pp. 1-49, 2021.

American Cancer Society. Atlanta: American Cancer Society, Inc. 2019.

Mohammed A. Al-masni, Mugahed A. Al-antari, Jeong-min Park, Geon Gi, Tae-Yeon Kim, Patricio Rivera, Edwin Valarezo, Mun-Taek Choi, Seung-Moo Han,Tae-Seong Kim. “Simultaneous Detection and ClassiÞcation of Breast Masses in Digital Mammograms via a Deep Learning YOLO-based CAD System”. Computer Methods and Programs in Biomedicine, vol. 157, pp. 85-94, 2018.

Hiba Asri, Hajar Mousannif, Hassan Al Moatassime, Thomas Noel. “Using Machine Learning Algorithms for breast Cancer Risk Prediction and Diagnosis”. Procedia Computer Science, vol. 83, pp. 1064-1069, 2016.

Fung Ting, Yen Jun Tan, Kok Swee Sim. “Convolutional Neural Network Improvement for Breast Cancer Classification”. Expert Systems With Applications, vol. 120, pp. 103-115, 2019.

Moloud Abdar, Mariam Zomorodi-Moghadam, Xujuan Zhou, Raj Gururajan, Xiaohui Tao, Prabal D Barua, Rashmi Gururajan. “A new nested ensemble technique for automated diagnosis of breast cancer”. Pattern Recognition Letters, vol. 132, pp. 123-131, 2020.

Lingxi Peng, Wenbin Chen, Wubai Zhou, Fufang Li, Jin Yang, Jiandong Zhang. “An immune-inspired semi-supervised algorithm for breast cancer diagnosis”. Computer Methods and Programs in Biomedicine, vol 134, pp. 259-265, 2016.

Manel Zribi, Younes Boujelbene. “Les réseaux de neurones un outil de sélection de variables : Le cas des facteurs de risque de la maladie du cancer du sein” . Éthique et économique, 9(1), 2012.

J.-E. Bibault, A. Burgun, P. Giraud. “Intelligence artificielle appliquée à la radiothérapie”. Cancer/Radiothérapie, vol. 21, pp. 239-243, 2017.

Or Herman-Saffar, Zvi Boger, Shai Libson, David Lieberman, Raphael Gonen, Yehuda Zeiri. “Early non-invasive detection of breast cancer using exhaled breath and urine analysis”. Computers in Biology and Medicine, vol. 96, pp. 227-232, 2018.

Sertan Kaymak, Abdulkader Helwan, Dilber Uzun. “Breast cancer image classification using artificial neural networks”. Procedia Computer Science, vol 120, pp. 126-131, 2017.

M. Boukhobza And M. Mimi. “Détection automatique de la présence d’anomalie sur une mammographie par réseau de neurones artificiels”. Courrier du Savoir, vol. 13, pp.103-108, 2012.

Simon Hadush Nrea, Yaecob Girmay Gezahegn, Abiot Sinamo Boltena, Gebrekirstos Hagos. “Breast cancer detection using convolutional neural networks”, 2020.

Mohamed Hosni, Ibtissam Abnane, Ali Idri, Juan M. Carrillo de Gea, José Luis Fernández Alemán. “Reviewing ensemble classification methods in breast cancer, Computer Methods and Programs in Biomedicine”, 2019.

Breast Cancer Wisconsin (Original) Data Set (BD1).

Breast Cancer Wisconsin (Diagnostic) Data Set (BD2).

Sulyman Age Abdulkareema, Zainab Olorunbukademi Abdulkareem. “An Evaluation of the Wisconsin Breast Cancer Dataset using Ensemble Classifiers and RFE Feature Selection Technique”. International Journal of Sciences: Basic and Applied Research (IJSBAR),Volume 55, No 2, pp 67-80, 2021.

M. Amrane, S. Oukid, I. Gagaoua and T. Ensar?. “Breast cancer classification using machine learning”. 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4. 2018.

S. K. Sarkar and A. Nag. “Identifying patients at risk of breast cancer through decision trees”. International Journal of Advanced Research in Computer Science, vol. 8, no. 8, pp. 88–91, 2017.

V. Chaurasia, S. Pal, and B. Tiwari. “Prediction of benign and malignant breast cancer using data mining techniques”. Journal of Algorithms & Computational Technology, vol. 12, no. 2, pp. 119–126, 2018.

M. M. Islam, M. R. Haque, H. Iqbal, M. M. Hasan, M. Hasan, and M. N. Kabir. “Breast cancer prediction: a comparative study using machine learning techniques”. SN Computer Science, vol. 1, no. 5, pp. 1–14, 2020.

Egwom, O.J., Hassan, M., Tanimu, J.J., Hamada, M., Ogar, O.M. “An LDA–SVM Machine Learning Model for Breast Cancer Classification”. Biomedinformatics, vol. 2, pp. 345–358, 2022.

Walid Theib Mohammad, Ronza Teete, Heyam Al-Aaraj, Yousef Saleh Yousef Rubbai, Majd Mowafaq Arabyat. “Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques”. Applied Bionics and Biomechanics, 2022.

Adel S. Assiri, Saima Nazir, Sergio A. “Velastin. Breast Tumor Classification Using an Ensemble Machine Learning Method”. J. Imaging ,2020.

Abdur Rasool, Chayut Bunterngchit, Luo Tiejian, Md. Ruhul Islam, Qiang Qu, Qingshan Jiang. “Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis”. nt. J. Environ. Res. Public Health, 2022.

MUAWIA. A. ELSADIG. “ENSEMBLE CLASSIFIER FOR BREAST CANCER DETECTION”. Journal of Theoretical and Applied Information Technology, vol. 100, pp. 3278-3287, 2022.

David H. Wolpert. “Stacked generalization”. Neural Networks, vol. 5, pp. 241-259, 1992.

Ludmila I. Kuncheva (2004, July 2). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. Available : [May 5, 2022].

le Cessie, S., van Houwelingen, J.C. “Ridge Estimators in Logistic Regression”. Applied Statistics, Vol. 41, pp. 191-201, 1992.

David G. Kleinbaum, Mitchel Klein (2010, July 1). Logistic Regression: A Self-Learning Text. (3nd edition). Springer.




How to Cite

Alio Boubacar Goga, Chaibou Kadri, Ibrahim Sidi Zakari, & Harouna Naroua. (2023). Detection and Diagnosis of Breast Cancer Using an Ensemble Statistical Learning Method. International Journal of Sciences: Basic and Applied Research (IJSBAR), 68(1), 198–209. Retrieved from