The Identification and Categorization of Anemia Through Artificial
  Neural Networks: A Comparative Analysis of Three Models
Mohammed A. A. Elmaleeh
This paper presents different neural network-based classifier algorithms for
diagnosing and classifying Anemia. The study compares these classifiers with
established models such as Feed Forward Neural Network (FFNN), Elman network,
and Non-linear Auto-Regressive Exogenous model (NARX). Experimental evaluations
were conducted using data from clinical laboratory test results for 230
patients. The proposed neural network features nine inputs (age, gender, RBC,
HGB, HCT, MCV, MCH, MCHC, WBCs) and one output. The simulation outcomes for
diverse patients demonstrate that the suggested artificial neural network
rapidly and accurately detects the presence of the disease. Consequently, the
network could be seamlessly integrated into clinical laboratories for automatic
generation of Anemia patients' reports Additionally, the suggested method is
affordable and can be deployed on hardware at low costs.