Differential Anemia Predation via Feed Forward Neural network

Abstract

Anemia is a one of the most common diseases which extensively scattered infamy of the least developing countries. The current methods of screening Anemia is carried out through complete blood tests received from clinical laboratory using manual approach for analyzing patient lead to high percentage of false positive test result of complete blood cell count (CBC) which can be reduced by employing intelligent Artificial Feed forward Neural Networks(FFNN).The proposed system was based on FFNN multi-layer perception (MLP) algorithm with 9 inputs, 50 neurons, and 1 hidden layer. It was used as the most powerful differential diagnosis instrument with accuracy as 88.0% for learning data, 87.5% for validation and test, and 86.5 % for overall networks. The FFNN detects anemia disease with high accuracy . Keywords: Anemia, complete Blood count, Feed Forward Neural Networks.