Breast Cancer Classification using Machine Learning Algorithm

• DOI: 10.1109/ickecs61492.2024.10616403
• Publication Date: 2024-08-07
• Authors: D Anu; K Vidhya
• Keywords: Statistical classification

Around the globe in this era breast cancer is the most common disease among women, previously detection of breast cancer was a long and tedious process. In the proposed system the patient’s data set will be collected. The collected data set will be manipulated and cleaned using feature extraction process [1]. The data set attributes consist of the nuclei measurements information. Nuclei measurements include radius, texture, perimeter etc. Also, the data set has the information whether the women breast does have the cancer or not by means of already calculated data which is indicated as benign or malignant. Benign are the non-cancerous tumor cell and that does not invade the neighboring cell, but malignant are the cancercausing tumor and it invades the neighbor cell after the manipulation and feature extraction of the data, it is further processed by machine learning algorithms under neural network. The dataset will be split into dependent and independent data. Dependent data are the data expect id and diagnosis, where the independent are diagnosis data [2]. The split data is $75 \%$ of training data and $25 \%$ is testing data. Then scale the data (feature scaling) to bring the training data to a particular range of 0 to 1. The logistic regression is a part of machine learning algorithm which is a subset of random forest classifier, confusion matrix, SVM classifier, KNN classifiers are made used for both training and testing the data set. With the result the accuracy will be calculated [3]. The highest accuracy method will be chosen and the result data and original data will be compare to find the accurate result that whether the women does have the cancer or not.