A Robust Model for Early Detection and Diagnosis of Breast Cancer using Linear Discriminant Analysis

Breast cancer ranks among the top causes of mortal-ity for women worldwide. The ability of current imaging modalities such as MRI, ultrasound and mammography to identify cancer and differentiate it from benign conditions is limited. Additionally, ionizing radiation exposure from routine mam-mograms can be concerning, particularly for younger women. Patient outcomes can be significantly improved by early detection, and by developing accurate preventive measures. In this article, we propose an innovative method for identifying breast cancer using Linear Discriminant Analysis (LDA). We maintained as much class-discriminatory information as possible while reducing the dimensionality of the data. This is especially helpful for high-dimensional data reduction without losing important information in healthcare diagnostics. Discriminative features are extracted from high-dimensional breast cancer data using LDA. Our suggested approach distinguishes benign versus malignant tumors from a dataset of mammography pictures and related clinical data. A classification algorithm that accurately detects cases of breast cancer was trained using the extracted features. Our model obtained an accuracy of 96.04% demonstrating the potential of LDA to be an effective tool for improving breast cancer early detection. This would lower the chance of fatalities by enabling prompt intervention and improved patient outcomes.