An LDA–SVM Machine Learning Model for Breast Cancer Classification

Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will expedite the treatment of this ailment. Recently, machine learning (ML) techniques have been employed in biomedical informatics to help fight breast cancer. Extracting information from data support clinical tedious time-consuming task. The use feature extraction has significantly changed whole process diagnosis. This research work proposed model for classification To achieve this, vector (SVM) was classification, linear discriminant analysis (LDA) extraction. We measured our model’s performance principal component (PCA) random forest classification. A comparative performed show effectiveness extraction, we computed missing values based on classifier’s accuracy, precision, recall. original Wisconsin Cancer dataset (WBCD) Prognostic (WPBC) were used. evaluated two phases: In phase 1, rows containing using mean, 2, median. LDA–SVM when median used compute better results, with accuracy 99.2%, recall 98.0% precision WBCD an 79.5%, 76.0% 59.0% WPBC dataset. SVM classifier had handling problems LDA applied as method computing values.