Breast Cancer Prediction using varying Parameters of Machine Learning Models
Puja Gupta, Shruti Garg
Abstract
Malignancy of tumor has caused major number of deaths among women. Machine learning tools with proper hyper parametric can help in identifying tumors efficiently. This paper presents six supervised machine learning algorithms such as k-Nearest Neighborhood, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine with radial basis function kernel. Deep learning using Adam Gradient Descent Learning was also applied because it combines the benefits of adaptive gradient algorithm and root mean square propagation. A unique hyper parametric change in each model is shown so that it gives better accuracy within the model as well as comparing each model with one other. The result of deep learning as the most accurate with minimum loss. The accuracy achieved by deep learning using Adam Gradient Descent Learning is 98.24%.