Machine Learning Applications for Automated Breast Cancer Detection and Analysis

Abstract

Breast cancer is the most secretive and common cancer among women and rarely in men.It is a vital issue to get the faster and accurate diagnosis of the patient so that doctors can decide the treatment in due time. Across the globe around 10% of the people are affected in some stage of their lives. Frequently occurring cancers are present especially among women. Most of the challenges are faced when the carcinoma or the cancer is not detected correctly at the initial stage by experts for medication. In the proposed research work, different Machine Learning techniques have been tried to get the most suitable accuracy for the analysis of breast cancer. Generally the traditional methods of data classification in the diagnosis have been effective in the days so far. The classification techniques used are in the form of decision tree, K-nearest neighbors, XG Booster, Ada Booster, Naive Bayes, Logistic Regression, SVM on Wisconsin Breast Cancer datasets, both before and after applying Principal Component Analysis. In this project supervised machine learning tool is used for detection of cancer.