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  • 标题:AN INTELLIGENT �AGENT BASED FRAMEWORK FOR LIVER DISORDER DIAGNOSIS USING ARTIFICIAL INTELLIGENCE TECHNIQUES
  • 本地全文:下载
  • 作者:H.RATNAMALA KIRUBA ; Dr.G.THOLKAPPIA ARASU
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2014
  • 卷号:69
  • 期号:1
  • 出版社:Journal of Theoretical and Applied
  • 摘要:Artificial intelligence is an emerging area of modern research that aims at infusing machine intelligence through computational techniques. Data mining (DM) enables efficient knowledge extraction from large datasets, in order to discover hidden or non-obvious patterns in data. Our motivation for using DM was based on the hypothesis that the application of the appropriate DM technique on patient records could form a suitable mechanism for the knowledge extraction representing the correlation between patient symptoms and disease. The extracted knowledge was then used for the provision of personalised recommendations to patients in collaboration with the agent-based framework developed. The agent � based system developed interacts with different modules of the overall integrated system developed to support liver disease diagnostic system. This research work aims at exploring the impact of machine learning techniques in liver disorder detection on two different datasets comprising of more than 900 patient records acquired from the University of California, Irvine, Machine Learning Repository . The findings revealed that C4.5 decision tree algorithm and the Random Tree algorithm produced 100 percent accuracy in classification of the liver disorders and we believe implementation of the proposed intelligent agent-based system will raise a precise and accurate diagnostic system for clinical ailments of diverse kind. To the best of our knowledge, this is the first attempt to explore this large collection of supervised machine learning techniques in the design of intelligent agent-based clinical systems for diagnostic purposes.
  • 关键词:Artificial intelligence; Intelligent Agents; Supervised learning; Data mining; Clinical diagnosis; Liver Disorder detection
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