AI-Based Smart Healthcare System for Early Breast Cancer Detection and Diagnosis

Abstract

Breast cancer is one of the leading causes of cancer-related deaths among women worldwide. Early detection and accurate diagnosis are essential for improving survival rates and reducing mortality. Recent advancements in Artificial Intelligence (AI) and machine learning have enabled the development of intelligent healthcare systems capable of assisting clinicians in the early identification of breast cancer with high accuracy. This study presents an AI-Based Smart Healthcare System for Early Breast Cancer Detection and Diagnosis that integrates advanced data preprocessing, feature selection, and machine learning techniques to classify breast tumors as benign or malignant. The proposed framework includes data cleaning, normalization, feature extraction, and classification using AI-based algorithms to enhance diagnostic performance while minimizing false predictions. Performance is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and confusion matrix to ensure the reliability and effectiveness of the proposed model. The developed smart healthcare system provides rapid, cost-effective, and accurate diagnostic support, thereby reducing the dependence on manual interpretation and facilitating timely clinical decision-making. Furthermore, the integration of AI into healthcare promotes personalized diagnosis, improves patient outcomes, and supports healthcare professionals in delivering efficient medical services. Experimental results demonstrate that the proposed system achieves high classification accuracy and has significant potential for real-world implementation in breast cancer screening and diagnosis. Overall, the proposed AI-based smart healthcare framework contributes to the advancement of intelligent medical diagnostic systems and offers a promising solution for enhancing early breast cancer detection and improving healthcare quality.