Using hierarchical soft computing method to discriminate microcyte anemia

Anemia is the most common hematological disorder. The complete blood count (CBC) is used to identify anemia and others disorder relative to hematology. However, discriminating both of iron deficiency anemias (IDA) and thalassemia (THA) depend on the mean cell volume (MCV) less than 80 fL (fluid ounces) that is imprecision and uncertain. Recently, more literatures applied soft computing methods to solved problem of classification under imprecision and uncertainty. This paper proposes a new approach which derived from soft computing, and rule-based, namely, hierarchical soft computing (HSC). HSC is fitting for discriminating microcyte anemia, which evaluated the performance of microcyte anemia diagnosis after ANFIS pruning rule found: (1) The 96% accuracy is inferred by ANFIS with 50 patterns, that is more accurate than traditional experience. (2) Both sensitivity (90.1%) and specificity (95.8%) are higher than discriminant function which has only higher either sensitivity or specificity. (3) The area under receiver operating characteristics curve (AUC) is 0.954 means that the accuracy is 95.4% when inference value is revised to 13.6. The HSC has been improved the performance of discriminant function to discriminate microcyte.