Breast Cancer Classification using Logistic Regression: An Efficient and Scalable Approach

Cancer is the foremost cause of non-accidental deaths worldwide. Among various types of Cancer, Breast cancer is the most frequent cause of cancer-related mortality. Breast cancer is a common and serious disease affecting women globally, ranking fourth among all cancers. Early detection and treatment of breast cancer can significantly improve prognosis and survival rates. Machine learning technology have become increasingly important in processing and analyzing massive amounts of medical data as healthcare technology has advanced. The study considered four classifiers such as Linear Support Vector Classifier (Linear SVC), Logistic Regression, Stochastic Gradient Descent Classifier (SGD Classifier) and Random Forest Classifier to determine the most effective model for predicting the presence of breast cancer based on accuracy score. The 400X Dataset from Wiscosin Breast Cancer dataset is used for study purpose. And the same dataset served as a training set for evaluating and comparing the effectiveness and efficiency of each algorithm based on classification accuracy. The results showed that the Logistic Regression classification algorithms gives highest accuracy of 97.3% when compared to other algorithms.