Abstract
Background: 8% of women are diagnosed with breast cancer. (BC) BC is the second most
common cause of death in both developed and undeveloped countries. BC is characterized by the mutation
of genes, constant pain, changes in the size, color (redness), and skin texture of breasts. Classification
of breast cancer leads pathologists to find a systematic and objective prognostic; generally, the
most frequent classification is binary (benign/malignant).
Introduction: Machine Learning (ML) techniques are broadly used in breast cancer classification.
They provide high classification accuracy and effective diagnostic capabilities. Breast cancer remains
one of the top diseases that lead to thousands of deaths in women yearly. Artificial intelligence (AI)
has been utilized to rapidly and accurately identify breast tumors and for early diagnosis. This paper
aims to research, determine and classify these tumors.
Methods: Machine learning algorithm such as Random Forest (RF) is used to classify medical images
into malignant and benign. Moreover, Machine learning has been employed recently for the same purpose.
Results: The results showed that Random Forest achieved high accuracy; therefore, the researchers
utilized various functions for this algorithm and added more features such as bagging and boosting to
increase its efficacy.
Conclusion: The random Forest algorithm achieved an enhanced accuracy of 98%.
Keywords:
Breast cancer, Machine Learning , Artificial Intelligence, Random Forest, Classification, SVM
Graphical Abstract
[1]
JesAs S, Omar B, Noel V, Luz A. Integration of data mining classification
techniques and ensemble learning for predicting the type of
breast cancer recurrence. Green, Pervasive, and Cloud Computing. Lecture Notes in Computer Science, Vol. 11484. Springer, Cham.2019; 18-30.
[2]
Uma O, Savita G. A study on prediction of breast cancer recurrence
using data mining techniques. International Conference on Cloud Computing,
Data Science & Engineering. 12-13 January 2017, Noida, India.
[3]
Ahmed I, Ahadur M, Shahed A, Shihabuzzaman S. Predicting breast
cancer recurrence using effective classification and feature selection
technique. 19th International Conference on Computer and Information
Technology (ICCIT). 18-20 December 2016, Dhaka, Bangladesh.
[5]
Animesh H, Subrata K, Amit G. Study and analysis of breast cancer cell detection using Nave Bayes, SVM and ensemble algorithms. Int J Comput Appl 2016; 145(2): 39-45.
[6]
Siham A, Sadeq Darrab M, Noaman SA, Saake G. Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. Communications in Computer and Information Science vol 1234 Springer, Singapore. 108-17.
[7]
Ahamed L, Sayeth S, Elankovan S, Azuraliza A. Comparative study on different classification techniques for breast cancer dataset. J Comput Sci Mob Computing 2014; 3(10): 185-91.
[8]
Vikas C, Saurabh P. A novel approach for breast cancer detection using data mining techniques. IJIRCCE 2014; 2(1): 2456-65.
[9]
Gouda I, Salama M, Abdelhalim M, Abdelghany Z. Experimental
comparison of classifiers for breast cancer diagnosis. Seventh International
Conference on Computer Engineering & Systems (ICCES),
27-29 November 2012, Cairo.
[10]
Lavanya D, Usha Rani K. Analysis of feature selection with classfication breast cancer datasets. Indian J Comput Sci Eng 2011; 2(5): 756-63.
[11]
Safia S, Geetha G, Leninisha S. Early diabetes discovery from tongue images. Comput J 2020; 65(2): 237-50.
[16]
Francisco M, Nuno N, Jacinto C. BreastScreening: On the use of multi-modality in medical imaging diagnosis. arXiv 2020; 2020: 2004.03500.
Download PDF Flyer