Investigating Algorithmic Techniques for Breast Cancer Detection

One serious health problem is breast cancer for women worldwide, and detecting it early can significantly improve treatment outcomes. Various methods and technologies are used to detect breast cancer, but finding the most accurate and reliable method remains a challenge. In this study, we focused on two popular machine learning algorithms, the SVM and the Decision Tree, to see which one is better at predicting the risk of breast cancer. Both of these methods have been used in similar tasks, but it is not always clear which one provides better results. While there has been some research comparing these algorithms, there is a need for more detailed studies using modern datasets to ensure the results are accurate and relevant to current medical practices. Our goal was to compare the effectiveness of the two algorithms in detecting breast cancer using a training dataset. We used a dataset with 10 samples for each algorithm to train and test their performance. SVM achieved an accuracy of 95.7% in detecting breast cancer, which was higher than the 93.07% accuracy of the Decision Tree algorithm. We used a significance level of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p&lt;0.05$</tex> to ensure our findings are statistically significant. The Support Vector Machine proved to be more accurate than the Decision Tree in predicting breast cancer risk.