Prediction of Blood Disorder and Cancer using Artificial Neural Networks: A Review

Abstract

Blood disorder and cancer is the one of the major health problem in all types of age group. The main aim of this paper is to predict the blood disorder and cancer. This paved a way to propose comparative study with previous studies based on artificial neural networks. The proposed work contains two levels such as feature selection and classification with 15 attributes. Blood disorder and cancer dataset collected from the Kaggle. First the Correlation Feature Selection (CFS) is formulate to identify the selected attributes for blood disorder and cancer prediction. Next all the data examine through the Radial Basis Function Neural Network (RBFNN) and find the centroid of each cluster using kernel k-means clustering algorithm. RBF has fast training process so it achieves high accuracy to predict the blood disorder and Cancer. According to this study, RBF is the best neural network approach than compared to other artificial neural network models.