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

arXiv:2404.04690v1 Announce Type: new
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 …

abstract algorithms analysis artificial artificial neural networks arxiv auto classifier classifiers comparative analysis cs.lg cs.sy eess.sy exogenous experimental identification linear network networks neural network neural networks non-linear paper study through type