Classification of cancer patients based on elemental contents of serums using bidirectional associative memory networks

Bidirectional associative memory (BAM) was applied for diagnosing cancer based on the elemental contents in serum samples. The serum samples were taken from clinical hospitals in north-east region of PR China and the elemental contents in serum samples were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES). The elemental contents of the sample were encoded to bipolar input values for BAM computation. The BAM method was verified with independent prediction samples by using the `cross-validationʹ method. The networks can be used to discriminate of all cancer patients from non-cancer patients at rate of 100%. A comparison study using BAM and multi-layer feed-forward neural network was made, better results were obtained using BAM networks. The effects of threshold values and output nodes of the BAM network were investigated and related problems were discussed. Results showed that the BAM would be applied to elemental analysis of serums and be promising method for diagnosis of cancer.