改良型遺伝的プログラミングを利用した投票モデルによる心疾患データベースからの危険因子の抽出 Extraction of Risk Factors from Coronary Heart Disease Database by Multi-agent Voting Model Using An Improved Genetic Programming

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Abstract

医療現場では,患者の検査結果に病気に特徴的な症状が観られるかを多面的に検証し,それらの結果を統合して診断を下している.本論文では,エージェント群の協調により,医療データベースから診断の根拠となる複数の危険因子を自動で抽出する手法を提案する.この手法は,自動グループ構成手法を利用したものであり,各エージェントが異なる観点から病気の局所的な症状を判定し,エージェント群の投票行動により情報を統合して診断を行う.本手法を心疾患データベースに適用し有効性を検証した.

In medical treatment, it is difficult to diagnose diseases directly from raw real value data obtained by medical examination for patients. For support of the task, it is necessary to transform the raw data into meaningful knowledge representations, which represent whether the characteristic symptoms of the disease are observed. In this research, we aim to acquire such multiple risk factors automatically from the medical database. We consider that a multi-agent approach is effective for extracting multiple factors. In order to realize the approach, we propose a new method using an improved Genetic Programming method, Automatically Defined Groups (ADG). By using this method, multiple risk factors are extracted, and the diagnosis is performed through multi-agent cooperative voting. We applied this method to the coronary heart disease database, and showed the effectiveness of this method.

Journal

  • Proceedings of the Fuzzy System Symposium

    Proceedings of the Fuzzy System Symposium 21(0), 85-85, 2005

    Japan Society for Fuzzy Theory and Intelligent Informatics

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