Advanced Predictive Analytics For EarlyStage Breast Cancer Diagnosis Leveraging Histopathological Data Integration

Predictive analytics refers to using machine learning models to analyze patterns in histopathological data and forecast the likelihood of early-stage breast cancer. This helps in identifying potential malignancies before they progress, enabling timely diagnosis and treatment. In existing system, the current breast cancer diagnostic system primarily relies on imaging techniques like mammography, ultrasound, and MRI, complemented by biopsy procedures for confirmation. While effective, these methods face challenges such as limited accuracy in dense tissues, operator dependency, and high false positive/negative rates. Genetic profiling andmolecular diagnostics provide deeper insights but are costly and complex. Recently, AI and machine learning have emerged to enhance diagnostic precision, yet issues like data bias and model transparency remain barriers to clinical adoption. The main aim of this paper is to develop a predictive model for breast cancer diagnosis using machine learning techniques and comprehensive data analysis. The Wisconsin Breast Cancer Dataset is used, containing tumor-related features labeled as benign or malignant. Data preprocessing includes handling null values, removing irrelevant columns, and visualizing class distribution and outliers. Feature selection is guided by correlation analysis, followed by data splitting and standardization. Models like Decision Tree, Random Forest, and Support Vector Classifier are implemented and evaluated using precision, recall, F1-score, ROC curves, and cross-entropy loss. A comparative analysis identifies the most effective model for accurate classification. This paper emphasizes the importance of early detection and contributes to enhancing diagnostic reliability through data-driven approaches.