Abstract
Millions of individuals live longer and healthier lives due to modern medical innovations. The advancement of modern medicine to combat numerous ailments has benefited humanity a lot. Breast cancer is one of the most fatal diseases affecting women globally. Early detection is essential for lowering the fatality rate. The objective of the paper is to create a computer-aided diagnostic model that helps in the early detection of breast cancer and hence decreases the death rate. The study introduces a hybrid strategy for effectively diagnosing breast cancer by using a Novel Relief algorithm for feature selection with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency of this proposed hybrid model and the ANFIS model without using any feature selection technique is estimated using the Wisconsin Breast Cancer Data set (WBCD). The study finds that the new hybrid model has attained the highest accuracy of 99.30% and is ideal for detecting breast cancer.
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