Decision Tree Algorithm for Breast Cancer Detection

Abstract

A major form of cancer affecting women around the world is breast cancer. This underscores the importance of early detection for optimal treatment outcomes. This paper addresses the challenge of correctly classifying tumors as malignant or benign in light of the fact that breast cancer is a significant component of cancer cases around the world. As a breast cancer detection algorithm, there are several advantages to using this decision tree algorithm. Decision trees provide insight into the importance of features, which in turn allows for the identification of key factors that contribute to the classification of breast cancer. In addition to that, decision trees are able to deal with both numerical and categorical features, so they are suitable for a variety of breast cancer data sets. It is also important to note that decision trees are less sensitive than other algorithms when it comes to outliers and missing data. To begin with, decision trees provide insight into the importance of features, which allows for the identification of key factors that contribute to the classification of breast cancers. A decision tree can also be used to analyze both numerical and categorical features, making it more versatile for the analysis of breast cancer data in general. The decision tree algorithm, on the other hand, has a lower sensitivity to outliers and missing data than some other algorithms. As a result of utilizing performance metrics to assess the effectiveness of algorithms, it was found that the Decision Tree Algorithm was more effective at detecting breast cancer than other algorithms.