The Identification and Categorization of Anemia Through Artificial Neural Networks: A Comparative Analysis of Three Models
This work addresses anemia diagnosis for clinical laboratories, offering an incremental improvement through comparative analysis of existing neural network methods.
The paper tackled the problem of diagnosing and classifying anemia by comparing three neural network models (FFNN, Elman, NARX) using clinical data from 230 patients, achieving rapid and accurate detection with potential for low-cost hardware deployment.
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.