Intelligent systems developed for the early detection of chronic kidney disease

  • Authors:

  • Ruey Kei Chiu;Renee Y. Chen;Shin-An Wang;Yen-Chun Chang;Li-Chien Chen

  • Affiliations:

  • Department of Information Management, Fu Jen Catholic University, New Taipei, Taiwan;Department of Information Management, Fu Jen Catholic University, New Taipei, Taiwan;Department of Information Management, Fu Jen Catholic University, New Taipei, Taiwan;En Chu Kong Hospital, Sanxia District, New Taipei City, Taiwan;Cardinal Tien Hospital, Xindian District, New Taipei City, Taiwan

  • Venue:
  • Advances in Artificial Neural Systems
  • Year:
  • 2013

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Abstract

This paper aims to construct intelligence models by applying the technologies of artificial neural networks including backpropagation network (BPN), generalized feedforward neural networks (GRNN), and modular neural network (MNN) that are developed, respectively, for the early detection of chronic kidney disease (CKD). The comparison of accuracy, sensitivity, and specificity among three models is subsequently performed. The model of best performance is chosen. By leveraging the aid of this system, CKD physicians can have an alternative way to detect chronic kidney diseases in early stage of a patient. Meanwhile, it may also be used by the public for self-detecting the risk of contracting CKD.