The Use of Inference Strategies in the Differential Diagnosis of Microcytic Anemia

A new expert system developed on a Macintosh personal computer using a commercially available artificial intelligence shell was compared with four different discriminant functions (DFs) for the differentiation of microcytic anemia into etiologic categories. Several databases were used with a different composition but all contained at least some samples from thalassemic individuals and from patients with iron deficiency anemia. The DFs analyzed were those proposed by England and Fraser, Green and colleagues, Mentzer, and by Shine and Lal. None of the databases performed satisfactorily when used singly, whereas very high false-positive rates were obtained by one of them. The diagnostic efficiency was somewhat improved by combining several DFs. An expert system using an artificial intelligence "shell" with an "interference engine" was developed using cluster analysis and a set of learning examples. The input necessary for the system to achieve a conclusion consists of MCV, RBC, and RDW as well as a statement as to whether the patient has anemia. Based upon the values of these parameters, the expert system will give an "advice" regarding the probabilities for thalassemia, iron deficiency, and/or other probabilities such as previous transfusions, anemia of chronic disease, laboratory error, etc. In a prospective trial, the system functioned with an accuracy of better than 85%.