Prediction of Breast Cancer Using Futuristic Approach

Kamal Bunkar¹ , Chhaya Arya²

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.5 , pp.55-59, Oct-2018

Online published on Oct 31, 2018

Copyright © Kamal Bunkar, Chhaya Arya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Breast Cancer is one of the diseases that causes a higher number of deaths in a year. Breast cancer is the second most common cause of mortality among women, and majority of death occur due to its unavailable fact in Canada. Breast cancer is the most treatable type of cancer among all others, and early detection and thorough screening for the disease guarantee a higher patient survival rate. In order to find a reliable approach of predicting breast cancer, this paper offers a study about breast cancer prediction based on machine learning techniques. This study compares numerous patient clinical records to find an accurate model that can predict the likelihood of developing breast cancer. In this paper, a few machine learning models— kNN (k Nearest Neighbour), SVM (Support Vector Machine), ANN (Artificial Neural Network), and Naive Bayes classifier—are used. Wisconsin Breast Cancer Database (1991) and Wisconsin Diagnostic Breast Cancer (1995) are two commonly used test data sets that are used to assess the performance of these models. The 10-fold cross-validation approach is utilised to calculate each model`s test error. We downloaded the dataset from Kaggle.com in order to conduct this study. With a class distribution of 357 benign and 212 malignant cells, it has a total of 32 attributes, including the I.D. number, diagnosis (M= malignant and B= benign), and additional 30 real-valued input properties. The analysis`s findings show a thorough trade-off between these tactics and also give a thorough assessment of the models. In practical use, it is anticipated that feature identification results will help doctors and patients prevent breast cancer.

Key-Words / Index Term :
Breast Cancer, Support Vector Machine, Random Forest, k-Nearest Neighbor, Artificial Neural Network

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