Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various Machine Learning Algorithms

Abstract

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. This research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector Machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI Machine learning laboratory. The G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector Machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. The significance value is arrived as 0.36 for this proposed system. The SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin Dataset.