title: Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data.
abstract: Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.
authors.affiliation: Amirkabir University of Technology, Tehran, Iran.
authors.forename: Iman
authors.initials: I
authors.lastname: Azarkhish
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Raoufy
authors.forename: Shahriar
authors.initials: S
authors.lastname: Gharibzadeh
mesh_terms: D000328:Adult; D000368:Aged; D018798:Anemia, Iron-Deficiency / Q000175:diagnosis*; D019411:Clinical Laboratory Techniques; D005260:Female; D005544:Forecasting; D017143:Fuzzy Logic; D006801:Humans; D007492:Iran; D007501:Iron / Q000097:blood*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer
chemical_list: D007501:Iron