A Comparison of ML Algorithms for Predicting Breast Cancer Risk using Health Data

The detection of breast cancer is an imperative clinical task. Still, it could be a timeconsuming and complicated task for medical analysis. The artificial neural network (ANN)plays an amazing part in both time optimization and accuracy, permitting the clinicals todistinguish between the patterns of malignant and benign. Here, we proposed an algorithmto find the effective methods for the breast cancer detection and classification using theANN, random forest (RF) and support vector machine (SVM). The proposed system iscomposed of different stages. Firstly, preprocessing of data is done using a normalizationmethod to remove the missing data in the dataset. At the end, classification is executed byANN and SVM algorithms. R platform has been used for the performance evaluation ofbreast cancer. The results demonstrate the methodology with 99.2% accuracy using ANNarchitecture.