Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer

  • Authors:

  • Wojciech Froelich;Elpiniki I. Papageorgiou;Michael Samarinas;Konstantinos Skriapas

  • Affiliations:

  • Department of Informatics and Material Science, Institute of Computer Science, University of Silesia, ul.Bedzinska 39, Sosnowiec, Poland;Department of Informatics and Computer Technology, Technological Educational Institute of LAMIA, 3rd Km Old National Road Lamia-Athens, Lamia 35100, Greece;Department of Urology, General Hospital of Larisa, Larisa, Greece;Department of Urology, General Hospital of Larisa, Larisa, Greece

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

The prediction of multivariate time series is one of the targeted applications of evolutionary fuzzy cognitive maps (FCM). The objective of the research presented in this paper was to construct the FCM model of prostate cancer using real clinical data and then to apply this model to the prediction of patient's health state. Due to the requirements of the problem state, an improved evolutionary approach for learning of FCM model was proposed. The focus point of the new method was to improve the effectiveness of long-term prediction. The evolutionary approach was verified experimentally using real clinical data acquired during a period of two years. A preliminary pilot-evaluation study with 40 men patient cases suffering with prostate cancer was accomplished. The in-sample and out-of-sample prediction errors were calculated and their decreased values showed the justification of the proposed approach for the cases of long-term prediction. The obtained results were approved by physicians emerging the functionality of the proposed methodology in medical decision making.