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

URI http://harp.lib.hiroshima-u.ac.jp/pu-hiroshima/metadata/12598
File
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
Title Alternative
Cancer Prediction of Medical Examination Data and Its Knowledge Extraction by Adaptive Structural Learning of Deep Belief Network
Author
氏名 鎌田 真
ヨミ カマダ シン
別名 Kamada Shin
氏名 市村 匠
ヨミ イチムラ タクミ
別名 Ichimura Takumi
氏名 原田 俊英
ヨミ ハラダ トシヒデ
別名 Harada Toshihide
Abstract

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 classification 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 find an initial cancer were reported in this paper.

Description

開催日:平成30年7月28日
会場:広島工業大学

Journal Title
2018 IEEE SMC Hiroshima Chapter若手研究会講演論文集
Spage
63
Epage
69
Published Date
2018
Publisher
IEEE SMC Hiroshima Chapter
Contributor
市村匠
Language
jpn
NIIType
Conference Paper
Text Version
出版社版
Set
pu-hiroshima