UWB-based early breast cancer existence prediction using artificial intelligence for large data set - Ahmad Ashraf, Abdul Halim; Veeraperumal, Vijayasarveswari; Andrew, Allan Melvin; Mohd Najib, Mohd Yasin; Mohd Zamri Zahir, Ahmad; Hossain, Kabir; Bari, Bifta Sama; Fatinnabila, Kamal (Penerbit Akademia Baru, 2023)
Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.