MARGARET OMAMUROMU ASHEDAGHO

Breast cancer stays one of the maximum standard and life-threatening illnesses affecting girls globally. Accurate and early diagnosis is critical for effective treatment and improved survival rates. This project explores the application of an Artificial Neuro-Fuzzy Inference System (ANFIS) for the classification of breast cancer. ANFIS combines the learning capabilities of neural networks with the reasoning capabilities of fuzzy logic, creating a hybrid model that can handle the complexities and uncertainties inherent in medical data. The research involves the collection and preprocessing of breast cancer datasets, followed by the design and implementation of an ANFIS model. The model is trained using a portion of the dataset and tested on the remaining data to evaluate its classification performance. Key performance metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve are used to assess the effectiveness of the ANFIS model. Preliminary results indicate that the ANFIS model demonstrates promising accuracy in distinguishing between benign and malignant breast tumors. The adaptive learning process of the ANFIS allows for continuous improvement and adjustment of the model, enhancing its diagnostic capabilities over time. This study highlights the potential of ANFIS as a reliable and efficient tool for breast cancer classification, contributing to the advancement of artificial intelligence applications in medical diagnostic