Mobile Agents Using Data Mining for Diagnosis Support in Ubiquitous Healthcare

Authors:
Romeo Mark Mateo;Louie F. Cervantes;Hae-Kwon Yang;Jaewan Lee
Affiliations:
School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea;School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea;School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea;School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea
Venue:
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Year:
2007

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Abstract

Recent research topics in healthcare including intelligent decision support services, expert medical services and autonomous management are based on multi-agent systems. The cooperation of these software agents provides efficient monitoring, analyzing, and managing the data of patient where abnormal patterns are detected to have an advance treatment and prevent loss of life. In this paper, a framework for ubiquitous healthcare based on multi-agent is presented. This paper proposes a mobile agent for diagnosis support in ubiquitous healthcare. The expert mobile agent (EMA) classifies the data of patient by using neuro-fuzzy algorithm for consultation report. A pre-processing method based on the profile of an expert is used to filter the data from the history of patient. Result of neuro-fuzzy from cross-validation test shows a high accurate classification in data compared to other highly accurate classifiers.