Breast cancer is a leading cause of mortality among women worldwide, particularly in developing countries. Accurate and early diagnosis is crucial to improve patient outcomes. This study compares the performance of three supervised machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF)—in classifying breast cancer cases using the Breast Cancer Wisconsin dataset. The dataset consists of 569 instances with 33 features, categorized into malignant and benign classes. Each method was evaluated based on its classification accuracy. The results show that Random Forest achieved the highest accuracy at 94.07%, outperforming SVM with 90.06% and KNN with 90.00%. The findings suggest that Random Forest provides the most reliable performance for breast cancer classification within the scope of this dataset. This study highlights the importance of selecting an appropriate algorithm to enhance diagnostic precision and recommends Random Forest as an effective method for similar classification tasks.

Detection of optic disc area is complex because it is located in an area that is considered as pathological blood vessels when in segmentation and thus require a method to detect the area of the optic disc, this paper proposed the optic disc segmentation using a method that has not been used before, and this method is very simple, K-means clustering is a proposed Method in this paper to detect the optic disc area with perfected using adaptive morphology. This paper successfully detect optic disc area quickly and segmented blood vessels more quickly.