Abstract:
Breast cancer affects approximately 10 percent of women around the world at some point in their lives, and has emerged as one of the most feared and most common cancers among women. The main dilemma occurs when the cancer cannot be properly identified in the initial stages. Machine learning in this area has proven to play a vital role in diagnosing diseases such as cancers. Methods of classification and identification of data that must be effective and an effective method for classifying data. Especially in the medical field, in this research classification techniques using the Automated Support Algorithm (SVM-RBF) were used on the breast cancer dataset at the University of Wisconsin. The main objective is to evaluate the accuracy of data classification with respect to the efficiency and effectiveness of the Automated Support Algorithm (SVM-RBF) in terms of accuracy, Precision, recall, specificity and F1 score. The experimental results showed that (Accuracy = 97.7%), (Precision = 97.7%), (Recall = 97.7%), (Specificity = 97.1%) and (F1 score = 97.7%)
