The Identification and Categorization of Anemia Through Artificial Neural Networks: A Comparative Analysis of Three Models

TL;DR

This study compares three neural network models for diagnosing and classifying anemia using clinical data, demonstrating rapid, accurate, and cost-effective detection suitable for clinical laboratory integration.

Contribution

It introduces a comparative analysis of neural network classifiers for anemia diagnosis, highlighting their accuracy and practicality for clinical use.

Findings

Neural networks accurately detect anemia from clinical data.

Proposed models are fast and suitable for real-time diagnosis.

Method is affordable and easy to implement in labs.

Abstract

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.