@inproceedings{oai:pu-hiroshima.repo.nii.ac.jp:00001442,
 author = {鎌田, 真 and KAMADA, Shin and 市村, 匠 and ICHIMURA, Takumi and 原田, 俊英 and HARADA, Toshihide},
 book = {2018 IEEE SMC Hiroshima Chapter若手研究会講演論文集},
 month = {},
 note = {application/pdf, Abstract?Deep Learning has a hierarchical network architecture to represent the complicated feature of in-put patterns. We have developed the adaptive structure learning method of Deep Belief Network (DBN) that can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and hidden layers in DBN. The proposed adaptive structure DBN was applied to the comprehensive medical examination data for the cancer prediction. The prediction system shows the highest classi?cation accuracy among the traditional DBN. In this paper, the explicit knowledge with respect to the relation between input and output patterns was extracted from the trained DBN network by C4.5. Some characteristics extracted in the form of If-Then rules to ?nd an initial cancer were reported in this paper., 開催日：平成30年7月28日　会場：広島工業大学},
 pages = {63--69},
 publisher = {IEEE SMC Hiroshima Chapter},
 title = {検診結果ビッグデータを用いた構造適応型Deep Belief Networkの癌予測システムと知識発見},
 year = {2018},
 yomi = {カマダ, シン and イチムラ, タクミ and ハラダ, トシヒデ}
}