Breast Cancer type detection by optimizing features using Genetic Algorithms and applying Probabilistic Neural Networks

Breast cancer is the second leading cause of cancer deaths worldwide. The Fine Needle Aspiration test (FNA) is a low-cost, easy and fast method for accurate and early diagnosis of breast cancer. In cases where it is not possible to determine with certainty whether the disease is benign or malignant, the use of artificial intelligence can help doctors in this field. In this study, the data of the WBCD database available at UCI, which includes ۶۹۹ benign and malignant breast tumor samples, was used. In this database, each sample has ۹ features. First, using the genetic algorithm, the optimal features were selected from among these nine features, and then the detection of the type of breast cancer was performed using a Probabilistic Neural Network (PNN). Simulations were performed in MATLAB ۲۰۱۸a. The simulation results showed that the algorithm proposed in this study can correctly diagnose the type of breast cancer in terms of whether it is benign or malignant with ۹۹.۵% accuracy, ۹۹.۴% specificity and ۹۷.۶% sensitivity.

کلیدواژه ها:

Breast cancer ، Genetic algorithm ، Probabilistic Neural Network ، Classification