Breast Cancer Detection Using Machine Learning Concepts

Abstract

Machine learning is applied in medical diagnosis to do early prediction of diseases, for increasing the possibility of recoverability around the globe. Cancer is a disease, which spreads quickly and would be difficult to control in advanced stages. The idea is to diagnose the disease at an early stage, so as to increase the chances of fast recovery. Breast cancer is common in women, and is a disease that causes the death of women in the age of fifty years or older. The purpose is to apply machine learning concepts to do early detection of disease. The system is fed with the images of all stages of cancer patients and the classification tools are used to train the system with the cases. This helps to predict the stage of cancer. After the prediction of the stage, the patient is prescribed with the medication or other appropriate treatment processes by the doctor. The right time diagnoses help to improve the prognosis and increase the chances of survival. The type of the tumour, size and its re-occurring nature need to be monitored from time to time to check it in control. The Data Mining algorithm in collaboration with Deep learning or Machine learning concepts can be used to design a system for early predictions. The proposal is to use the machine learning concepts to do performance comparison using different classifiers, such as Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbour (KNN) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset [1]. The main aim of cancer detection is to classify tumours into malignant or benign, thus we use machine learning techniques to improve the accuracy of diagnosis. The main objective is to assess the efficiency, effectiveness and correctness of the algorithm using performance metrics like Accuracy, Precision, F1 score and Recall Experimentation is done using Jupyter Notebook.