Towards Precision Oncology: A Study of Machine Learning Models for Breast Cancer Prediction
L. A. Anto Gracious, P. J. Beslin Pajila, G. Johncy, T. P. Anish, K. Prasanth, Siva Subramanian R · 2024
The rapid growth in Breast Cancer (BC) related death is frightening, making it the most prominent cancer type and a cause of death of women worldwide. It is most important to have advancements in predicting and diagnosing cancer for promoting a healthier life. To increase the advancement in treatment and to increase the survivability of patients it is important to achieve a high level of accuracy in cancer prediction. The ML techniques have given a huge contribution in the prediction process and diagnosing BC, gathering noticeable attention in research, and demonstrating the desired result. The ML algorithms like SVM, RF, LR, C4.5, KNN are applied to the BC Wisconsin Diagnostic dataset. After getting the results, performance evaluation and comparisons were conducted among the various classifiers. The aim of the research paper is to find out and diagnose the BC with the help of the applications of ML algorithms, and with the most effective factors like recall, F-measure accuracy, and precision. SVM gave a good performance comparing to the other classifies by achieving the accuracy of 97.2%. The research describes the role of ML in improving the BC prediction and diagnosis, and providing a clear understanding about the effectiveness of different algorithms to improve the health conditions.