Improving Breast Cancer Prediction Accuracy Using Classical and Advanced Machine Learning Algorithms

To forecast the outcomes of patients with tumors, machine learning (ML) methods have been employed in the construction of categorization models. To be more precise, binary classification has been utilized to control whether a tumor is malignant or benign. An analysis of numerous conventional techniques for breast this article presents cancer extrapolation. A sizable dataset of 1.8 million records from the NIH database was used in the investigation. Both deep learning and conventional machine learning methods’ accuracy was assessed. Classical methods included support vector machines (SVM), ensemble techniques (ET), decision trees (DT), and linear discriminates (LD). In addition, advanced methods like PNN were measured for comparison.