Machine Learning-Based Approaches for Breast Cancer Prediction Model: Study and Evaluation of Performance
Vikas Solanki, Anmol Ghai, Ravi Kumar Sachdeva, Amit Chaudhary · 2023
Women die worldwide frequency rate from breast cancer also called breast carcinoma is very high in today's era. In this research, machine learning (ML) methods for a breast cancer prediction model are studied and their performances are compared. Breast cancer is a serious and prevalent disease, and early detection of breast carcinoma is critical task that may help health professionals for successful treatment. ML algorithms have been applied to medical imaging data to assist in the early detection of breast cancer. The study analyzes the performance in terms of the accuracy, precision, and Fl-score of four machine learning algorithms (MLA) which include SVC, Naive Bayes, Random Forest and KNN (K-Nearest Neighbor). The study uses a publicly available dataset of breast cancer patients, which includes various clinical and demographic variables. The study provides insights into the performance of Random Forest, Naive Bayes, SVC, and KNN different MLA for detecting breast carcinoma and can assist in the development of more accurate and reliable breast cancer detection models. According to the findings, the SVC performs more accurately than the other algorithms.