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 to distinguish between the patterns of malignant and benign. Here, we proposed an algorithm to find the effective methods for the breast cancer detection and classification using the ANN, random forest (RF) and support vector machine (SVM). The proposed system is composed of different stages. Firstly, preprocessing of data is done using a normalization method to remove the missing data in the dataset. At the end, classification is executed by ANN and SVM algorithms. R platform has been used for the performance evaluation of breast cancer. The results demonstrate the methodology with 99.2% accuracy using ANN architecture.