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

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.

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► The prediction of prostate cancer using fuzzy cognitive maps (FCMs) is proposed.
► The long prediction horizon requires an enhancement of the FCM learning algorithm.
► The improved efficiency of the long-term prediction is achieved.