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Detection and Classification of Anemia using Artificial Neural Networks: Comparison of Three Models
(Graduate College, 2017-10-01) Esra Mohammed Ibrahim, Mohamed A. A. Elmaleeh; Daalia Mahmoud
This paper presents different classifier algorithms neural network to diagnose and classify anemia and compare these current classifier with feed forward back propagation, Elman network and Non-linear Auto-Regressive exogenous model . The results obtained from a range of models while conduction the experiments. The proposed network is trained by using the data received from clinical laboratory test results for 230 patients. The network has nine inputs (age, gender, RBC, HGB, HCT, MCV, MCH, and MCHC, WBCs) and an output. The simulation results obtained for different patients show that the proposed artificial neural network detects the disease very fast, and precise. Therefore this network can be implemented to automatically present the anemia patients’ reports in the clinical laboratory. The proposed technique can be implemented in hardware with minimal cost.