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.

本文介绍了用于诊断和分类贫血的不同基于神经网络的分类器算法，并将这些分类器与已建立的模型（如前馈神经网络（FFNN）、Elman网络和非线性自回归外生模型（NARX））进行了比较。通过使用来自230名患者的临床实验室检测结果的数据进行实验评估。所提出的神经网络具有九个输入（年龄、性别、RBC、HGB、HCT、MCV、MCH、MCHC、WBCs）和一个输出。对于不同的患者进行仿真结果表明，建议的人工神经网络能够快速准确地检测出疾病的存在。因此，该网络可以无缝地集成到临床实验室中，自动生成贫血患者的报告。此外，该建议方法价格实惠，可以在低成本的硬件上部署。

通过人工神经网络对贫血进行鉴定和分类：三种模型的比较分析