This report analyzes a research paper on data mining techniques applied to intelligent systems, specifically focusing on breast cancer detection. The report delves into the background of the research, highlighting the significance of breast cancer and the challenges in analyzing related data. It discusses the methods used, including three decision tree classifiers: Sequential Minimal Optimization (SMO), IBK (K-nearest neighbors), and Best First trees, along with the WISCONSIN dataset and Weka toolkit. The findings section details the classification of the dataset, experimental results, and the evaluation criteria used. The report also addresses the issues and limitations, such as the static nature of the data, and concludes by summarizing the application of data mining techniques and their effectiveness in achieving the research objectives. The report also includes a comprehensive list of references.