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Disease Modeling Using Evolved Discriminate Function

  • James Cunha Werner
  • Tatiana Kalganova
Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)

Abstract

Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model.

Keywords

Breast Cancer False Negative Discriminate Function Receiver Operating Characteristic Collagen Disease 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • James Cunha Werner
    • 1
  • Tatiana Kalganova
    • 1
  1. 1.Department of Electronic & Computer EngineeringBrunel UniversityUxbridgeMiddlesex

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