On detection of confused blood samples using self-organizing maps and genetic algorithm

Abstract

A SOM (self-organizing map)-based detection of confusion of blood test data referred to as CBC (complete blood count) data is proposed. Firstly, the method based on only SOM is shown. The learning data applied to SOMs are generated by subtracting the immediately anterior CBC data of subjects from the present CBC data. All the neurons in the second layer of SOM trained by applying the above learning data are roughly divided into the following two clusters: a cluster with neurons reacting to regular input data, and a cluster reacting to irregular input data which are generated by subtraction between confused CBC data. So, if the firing neuron belongs to the latter cluster, it is presumed that the confusion arises among CBC data of some subjects. Next, a method based on both SOM and GA (genetic algorithm) is shown. With the exception of selecting some elements, which instruct the weights to be updated in the second layer of CBC data by means of GA, the learning and the detection strategy adopted by this method are similar to those by the firstly proposed method. Experimental results on detecting the confusion, which arises among CBC data of 750 subjects, show that the second proposed method produces the second layer which achieves the high accuracy of detection especially when the input data, not to be employed during the learning, are applied.