Early Prediction of Breast Cancer by using Artificial Neural Network and Machine Learning Techniques

Acoustic For a limited number of disorders, early diagnosis of any illness may be curable to mankind's commitment. Before it becomes chronic, most persons fail to detect their illness. This adds to a global rise in mortality rates. Breast cancer is one of the cancers that can be treated until it progresses to all areas of the body as the condition is diagnosed at early stages. Breast cancer primarily affects women and it is also an important factor in raising the rate of female mortality. We are both mindful that the diagnosis of breast cancer is very time-consuming. On the other hand, the availability of technology used to diagnose early-stage cancer is very limited. Different algorithms for Machine Learning and Deep Learning have been used to distinguish benign and malignant tumors. UCI Wisconsin database containing 569 samples and 31 features is included in these papers. The paper focuses on numerous models that are applied to the dataset taken, such as K Nearest Neighbor, Support Vector Machine, Random Forest, Naive Bays, Logistic Regression, Gradient Booster, and Artificial Neural Network (ANN), etc. In terms of accuracy, cross-validation, sensitivity, and specificity gained, each of these algorithms was calculated and compared. From the experiments, we come to the solution that Random Forest gives the best accuracy is 98.83% and the and the worst algorithm K-nearest Neighbors accuracy is 91.22%. Deep learning algorithms ANN have been applied to improve prediction accuracy. The overall accuracy reached in the case of ANN 99.73%, respectively. There are two types of activation functions used for forecasting namely Relu and Sigmoid.