Predicting Breast Cancer Intelligently with Machine Learning Techniques

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, necessitating early and accurate detection to improve survival rates. This study presents an intelligent breast cancer prediction framework using advanced machine learning techniques. The proposed system integrates clinical, imaging, and diagnostic datasets to identify patterns associated with benign and malignant tumours. Data preprocessing techniques, including normalization and missing value handling, are applied to ensure data quality. Feature selection methods are employed to extract the most relevant attributes influencing prediction performance. Multiple machine learning algorithms, such as Support Vector Machine (SVM), Random Forest, Naïve Bayes, Logistic Regression, and K-Nearest Neighbors (KNN), are implemented and compared. The models are evaluated using performance metrics including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). Hyper parameter tuning and cross-validation techniques are utilized to enhance model robustness and generalization. Experimental results indicate that ensemble learning methods, particularly Random Forest, achieve superior prediction accuracy compared to other models. The proposed approach demonstrates the potential of intelligent systems in supporting early diagnosis and clinical decision-making. This research contributes to the development of reliable and efficient breast cancer prediction systems, ultimately aiding healthcare professionals in improving patient outcomes.