Quantitative Nuclei Analysis for Accurate Detection of Breast Abnormalities through Machine Learning

This research explores the essential domain of breast cancer detection by leveraging the capabilities of machine learning algorithms. Breast cancer, a critical global concern, necessitates early identification for effective patient outcomes. This study merges machine learning and medical research for more precise breast cancer detection. Our approach employs machine learning algorithms to differentiate malignant and benign breast cancer cells using quantifiable nuclei attributes from fine needle aspiration images. The dataset contains 569 samples, each with 32 features. Algorithms include AdaBoost, XGBoost and K-Nearest Neighbors with comprehensive data preprocessing including scaling and handling missing values. Techniques like PCA and SMOTE handle class imbalance and high dimensionality. Across scenarios, XGBoost consistently outperforms AdaBoost and KNN in accuracy, precision, recall, and F1-score. This research underscores the potential of machine learning in enhancing breast cancer diagnosis, contributing to advanced methodologies that emphasize the significance of quantitative nuclei attributes in accurate detection.