A Hybrid Model Based on Artificial Neural Networks and Genetic Algorithms for Anemia Prediction

A new model based on the combination of artificial neural networks and genetic algorithm for anemia prediction problems is presented. The goal of the algorithm to estimate anemia types by using data collected from laboratories based on blood variables for 560 subjects. ANN is used to classify the types of anemia and then use the GA to enhance the performance of the ANN to increase the classification accuracy. This process is carried out by enhancing the weights of neurons for the ANN to be more suitable for recognizing anemia types. The results confirm that the performance of the combined algorithm with a hidden layer is the most appropriate architecture among the networks with a different number of hidden layers. Results showed that the hybrid algorithm provides a highly precise result for the classification of anemia types, where the algorithm has an accuracy of 95.1% and the baseline algorithm has an accuracy of 91.8%, then the proposed algorithm may be considered to be clinically significant. Those results confirm that predicting anemia types is of great importance in health-related education and applications. Based on the biophysical factors, this study is provided a better understanding of the importance of predicting anemia.