Breast Cancer Detection using Macchine Learning Algorithms - Diaa SALAMA ABD ELMINAAM; Shaza BAHNACY; Esraa HASSAN; Habiba TAREK; Mai HAZEM; Nura MOSTAFA; Lobna SHAHEEN (Communications of International Proceedings, 2020)
Nowadays, Breast cancer is a profoundly severe disease that many women have been diagnosed with in the past few decades. For starters, Breast Cancer is mainly the formation and grouping of some malignant cancerous cells inside the breast cells. Even though breast cancer is the second most cancerous disease that women get diagnosed with, Men also can get diagnosed with it, but it is not very likely. Breast cancer can be deadly if not detected quickly and in its early phases. Subsequently, scientists have been searching for a method to help them address this problem efficiently, and their studies lead them to machine learning. Recently, Breast cancer Prediction has been one of the most progressing topics in machine learning. In the proposed paper, we quickly disclosed and identified whether a tumor is benign or malignant with some features processing of our 2 benchmark datasets and classifying algorithms (KNN, decision tree, naïve Bayes). A malignant tumor is a cancerous tumor that spreads, and the benign is the tumor that does not spread and is not cancerous.  To conclude, as for the first dataset, we found that the best algorithm was the IBK  Accuracy of 95.43%. We found that the naïve Bayes algorithm proved better than the other algorithms for the second dataset due to its relatively good data71.67%.
Keywords: Malignant; Benign; Classification; and machine learning Algorithms