PREDICTING BREAST CANCER USING MACHINE LEARNING

Early and accurate detection of breast cancer is crucial for public health. This research explores using machine learning (ML) to address this challenge. The goal is to develop models that can analyze clinical data and differentiate between benign and malignant cases. By evaluating different ML algorithms, the study hopes to identify effective tools for breast cancer diagnosis. This strategy includes data processing, feature selection, developing a complete model, and scoring it with the WBCD dataset. The study involved a comprehensive ensemble of ML classifiers: logistic regression, decision trees, random forest, and support vector machine. We operationalize these models into careful experiments and evaluations, preserving accuracy, precision, recall, and F1-score together with AUC. However, the results provide a hint for testing the ML model on breast cancer diagnosis. SVM was one of the best models able to attain the highest accuracy rate, highest precision, highest recall, and highest F1. The other models, like logistic regression and random forest, used to show good performances, whereas decision trees showed interpretability advantages with a little lower accuracy. This finding will be applicable as a substitution in early detection and intervention for breast cancer, in the long run for better patient outcomes, and the advancement of medical diagnostics using ML techniques. Much interdisciplinary effort, therefore, is necessary to provide robust diagnostic tools with an understanding of breast pathologies. Much work is expected to be focused on further fine-tuning the ML models, integration of such multimodal data, and clinical validation to the highest standard so that it becomes easy to use