Classification of Breast Cancer Detection Using K-Nearest Neighbor Algorithm Trained with Wisconsin Dataset

Abstract

Cancer is a type of diseases, which is caused by changes in body cells and a huge increase in number of their control and growth. Prediction of breast cancer is necessary to increase the mortality rate of those patients who are suffering from this breast cancer. Breast cancer diagnosis required analysis of medical images. There are many breast cancer detection algorithms has been proposed like SVM, KNN,K-MEAN, RAMDON FOREST, NAVIE BAYES.The most invasive cancer in females: breast cancer is affecting 12% for population worldwide. Breast cancer has no physical symptoms unless there is sign of painless lump. Breast cancer pathology images are classified into: normal, benign and malignant. Breast cancer cell have sub classes interrelated to cells’ variability, organization and density with the structure of tissues and morphology.Eventually various studies were systematically reviewed related articles published from 2010 to 2020 were considered.Result is calculated based on performance metric elements: Accuracy, sensitivity. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using Machine learning methodology (KNN) for the diagnosis and training purpose, it is supervised machine learning technique; the initial step of this classifier is to calculate distance of the input test sample with all the dataset entries of training samples. The best accuracy achieved from KNN is 96.49%.