Detecting breast cancer using machine learning algorithms: The efficient and accurate way

Abstract

Breast cancer is one of the most prevalent diseases that claims the lives of thousands of women every year. Artificial intelligence has been used to identify breast cancer early, fast, and correctly (AI). The objective of this essay is to assess current classification work on these tumours. Using machine learning techniques like Support Vector Machine (SVM), K Nearest Neighbor (K-NN), and Random Forest, medical pictures are divided into benign and malignant categories (RF). Convolutional Neural Network C Nearest Neighbor (CNN) is one of the deep learning techniques recently employed for comparable purposes. Due to its high mortality and morbidity rates, breast cancer presents a particular concern to female patients. Therefore, it is essential to have an algorithm that can recognise the early symptoms of breast cancer. In order to predict breast cancer, the results were assessed using the four techniques: Convolutional Neural Network, Decision Trees, Logistic Regression and random forests is essential for identifying the early signs of breast cancer. Three distinct classification ML techniques will be employed in this investigation. The effectiveness and accuracy of each algorithm will next be assessed. For classification systems, data with unbalanced classes constitute a substantial problem, requiring careful management and pre-processing. Using a dataset of breast cancer patients, we'll train a variety of machine learning models. The best solution for this issue is finally found by evaluating the accuracy and performance of each algorithm. In order to choose the most effective course of action, this research will display the effectiveness of multiple ways for categorising breast cancer.