MACHINE LEARNING TECHNIQUES TO DETECT BREAST CANCER
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Abstract
Breast Cancer is one of the regular diseases in ladies as well as in scarcely any men. As indicated by explore, the death pace of females has expanded chiefly on account of Breast Cancer tumor. One out of each eight ladies and one out of each thousand men are determined to have breast malignancy. Breast cancer tumors are for the most part grouped into two kinds: Benign tumor which is a non-dangerous tumor and other one is harmful tumor which is a malignant tumor. So as to realize which kind of tumor a patient has; the exact and early conclusion is an extremely significant advance. ML calculations have been utilized to create and prepare the model for arrangement of the sort of tumor. For exact and better grouping a few characterization calculations in ML have been prepared and tried on the dataset that was gathered. As of now calculations like Naïve Bayes, Random Forest, K-Nearest Neighbor and SVM demonstrated better precision for order of tumor. At the point when we executed Multilayer Perceptron (MLP) calculation it gave us the best precision levels among all both during training and testing. Mlp algorithm gave an accuracy of 97%. Along these lines, the specific arrangement utilizing this model will assist the specialists with diagnosing the sort of tumor in patients rapidly and precisely.
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