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, WDBC.
Graphical Abstract
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