Background and Aims: Breast cancer is considered as a general life-threatening state which considerably engages females all around the world. In ۲۰۲۰, breast cancer caused ۶۸۵,۰۰۰ deaths in the world and it has a significant role in cancer deaths. The detection of cancer cells in the early-stage of the disease can effectively decrease death rates and leads to a better prognosis. Many approaches exist for the diagnosis of breast cancer, but the problem is that these approaches possess many limitations. Artificial intelligence is shown to have the ability to analyze breast images for the detection of breast cancer. Our principal goal was to utilize artificial intelligence techniques to provide solutions for this problem. Therefore, we proposed using machine learning algorithms as an appropriate way to detect breast cancer. Methods: We aimed to classify tumors into benign and malignant groups. We used the
Breast Cancer Wisconsin Diagnostic Dataset. The dataset was obtained from Kaggle and contains ۵۶۹ tumor samples and ۳۰ numerical features, including radius, smoothness, and perimeter of tumor and etc. These features are based on digital images of fine needle aspirate biopsy slides. We trained and fine-tuned ۹ machine learning algorithms. The models were assessed using metrics like accuracy, F۱-score, and the area under the receiver operating characteristic curve. Results: The Support Vector Machine achieved the accuracy of ۹۸.۹۸%, which was the highest one, considering that Logistic Regression gained similar accuracy but Support Vector Machine gained higher area under the receiver operating characteristic curve. Gradient Boosting with the accuracy of ۹۷.۹۷% ranked next. Neural Network and K-Nearest Neighbors achieved ۹۶.۹۶%, Extreme Gradient Boosting and Random Forest gained ۹۵.۹۵%, Naïve Bayes achieved ۹۳.۹۳%, and Decision Tree with the accuracy of ۸۶.۸۶% ranked last. Conclusion: Machine learning algorithms could achieve high accuracy in classifying breast masses. Findings can help physicians in the early detection of breast cancer. Future studies can achieve better results by using larger sample size and more features.
