A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis.

Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature. [ABSTRACT FROM AUTHOR]