Breast Cancer Classification using Decision Tree Algorithms

Abstract

Cancer is a major health issue that affects individuals all over the world. This disease has claimed the lives of many people, and will continue to do so in the future. Breast cancer has recently surpassed cervical cancer as the most frequent cancer among women in both industrialized and developing countries and it is now the second leading cause of cancer mortality among women. A high number of women die each year as a result of this disease. Breast cancer is significantly easier to treat if caught early. This paper introduces a decision tree-based data mining technique for breast cancer early detection with highest accuracy, which helps patients to recover. Breast cancers are classed as benign (unable to penetrate surrounding tissue) or malignant (able to infiltrate adjacent tissue) breast growths. Two tests were included in the review. The primary study uses 10 breast cancer samples from the Kaggle archive, whereas the follow-up study uses 286 breast cancer samples from the same pool. The Decision Tree's accuracy in the first trial was 100%, while it was 97.9% in the follow-up inquiry. These findings justify the use of the proposed machine learning-based Decision Tree classifier in pre-evaluating patients for triage and decision-making prior to the availability of data.