Detection Of Breast Cancer Based on Fuzzy Logic
G. Neelima, P Kanchanamala, Alok Misra, Ryan Adhitya Nugraha
Abstract
The most dangerous illnesses in medical history are said to include breast cancer. People die more frequently because breast cancer, one of the leading causes of death worldwide, is more common. However, early detection of this cancer helps to save lives. This is complicated because mammography detection is difficult in many ways. A system for early detection and diagnosis of breast cancer is being developed for physicians using Machine Learning (ML) techniques, which has the potential to significantly improve survival rate of patients. Using machine learning algorithms, fuzzy-based breast cancer detection is demonstrated in this analysis. This analysis proposes two machine learning methods for the Wisconsin (Diagnostic) Data Set: the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. In consideration of the parameters of precision, accuracy, specificity, and recall, the combined model outperforms other individual Machine Learning models. Fuzzy based SVM and DT classification technique efficiently detects the breast cancer diseases with high Accuracy as 98.2%, Precision, 97.6%, Recall 96.5% and Specificity 97.8%.