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Deep Learning has a hierarchical network architecture to represent thecomplicated feature of input patterns. The adaptive structural learning methodof Deep Belief Network (DBN) has been developed. The method can discover anoptimal number of hidden neurons for given input data in a Restricted BoltzmannMachine (RBM) by neuron generation-annihilation algorithm, and generate a newhidden layer in DBN by the extension of the algorithm. In this paper, theproposed adaptive structural learning of DBN was applied to the comprehensivemedical examination data for the cancer prediction. The prediction system showshigher classification accuracy (99.8% for training and 95.5% for test) than thetraditional DBN. Moreover, the explicit knowledge with respect to the relationbetween input and output patterns was extracted from the trained DBN network byC4.5. Some characteristics extracted in the form of IF-THEN rules to find aninitial cancer at the early stage were reported in this paper.

Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5