Wojciech Froelich

Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps

The task of prediction in the medical domain is a very complex one, considering the level of vagueness and uncertainty management. The main objective of the presented research is the multi-step prediction of state of pulmonary infection with the use of a predictive model learnt on the basis of changing with time data. The contribution of this paper is twofold. In the application domain, in order to predict the state of pneumonia, the approach of fuzzy cognitive maps (FCMs) is proposed as an easy of use, interpretable, and flexible predictive model. In the theoretical part, addressing the requirements of the medical problem, a multi-step enhancement of the evolutionary algorithm applied to learn the FCM was introduced. The advantage of using our method was justified theoretically and then verified experimentally. The results of our investigation seem to be encouraging, presenting the advantage of using the proposed multi-step prediction approach.

Application of Evolutionary Fuzzy Cognitive Maps for Prediction of Pulmonary Infections

In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed to cope with the forecasting of the patient states in the case of pulmonary infections. The goal of the research was to improve the efficiency of the prediction. This was succeeded with a new data fuzzification procedure for observables and optimization of gain of transformation function using the evolutionary learning for the construction of FCM model. The approach proposed in this paper was validated using real patient data from internal care unit. The results emerged had less prediction errors for the examined data records than those produced by the conventional genetic-based algorithmic approaches.

Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series

Abstract Time series are built as a result of real-valued observations ordered in time; however, in some cases, the values of the observed variables change significantly, and those changes do not produce useful information. Therefore, within defined periods of time, only those bounds in which the variables change are considered. The temporal sequence of vectors with the interval-valued elements is called a ‘multivariate interval-valued time series.’ In this paper, the problem of forecasting such data is addressed. It is proposed to use fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model. Using interval arithmetic, an evolutionary algorithm for learning FGCMs is developed, and it is shown how the new algorithm can be applied to learn FGCMs on the basis of historical time series data. Experiments with real meteorological data provided evidence that, for properly-adjusted learning and prediction horizons, the proposed approach can be used effectively to the forecasting of multivariate, interval-valued time series. The domain-specific interpretability of the FGCM-based model that was obtained also is confirmed.

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 patients 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.

Predictive Capabilities of Adaptive and Evolutionary Fuzzy Cognitive Maps - A Comparative Study

Fuzzy cognitive maps (FCMs) represent the decision process in the form of a graph that is usually easy to interpret and, therefore, can be applied as a convenient decision-support tool. In the first part of this chapter, we explain the motivations for the research on FCMs and provide a review of the research in this area. Then, as stated in the title of the chapter, we concentrate our attention on the comparative study of adaptive and evolutionary FCMs. The terms adaptive and evolutionary refer to the type of learning applied to obtain a particular FCM. Despite many existing works on FCMs, most of them concentrate on one type of learning method. The purpose of our research is to learn FCMs using diverse methods on the basis of the same dataset and apply them to the same prediction problem. We assume the effectiveness of prediction to be one of the quality measures used to evaluate the trained FCMs. The contribution of this chapter is the theoretical and experimental comparison of adaptive and evolutionary FCMs. The final goal of our research is to determine which of the analyzed learning methods should be recommended for use with respect to the considered prediction problem. To illustrate the predictive capabilities of FCMs, we present an example of their application to the prediction of weather conditions.

Dynamic optimization of fuzzy cognitive maps for time series forecasting

In this paper we propose a new approach to learning fuzzy cognitive maps (FCMs) as a predictive model for time series forecasting. The first contribution of this paper is the dynamic optimization of the FCM structure, i.e., we propose to select concepts involved in the FCM model before every prediction is made. In addition, the FCM transformation function together with the corresponding parameters are proposed to be optimized dynamically. Finally, the FCM weights are learned. In this way, the entire FCM model is learned in a completely new manner, i.e., it is continuously adapted to the current local characteristics of the forecasted time series. To optimize all of the aforementioned elements, we apply and compare 5 different population-based algorithms: genetic, particle swarm optimization, simulated annealing, artificial bee colony and differential evolution. For the evaluation of the proposed approach we use 11 publicly available data sets. The results of comparative experiments provide evidence that our approach offers a competitive forecasting method that outperforms many state-of-the-art forecasting models. We recommend to use our FCM-based approach for the forecasting of time series that are linear and tend to be trend stationary.

Hybrid approach to the generation of medical guidelines for insulin therapy for children

To support doctors in planning insulin therapy for juvenile diabetic patients, we propose in this paper a new approach to the generation of formal medical guidelines. In the first stage of our approach, we cluster static patients data to obtain patient cohorts with similar medical characteristics. In the second stage, for every patient of the cohort, we model the course of the disease. Using discretization and a symbolic time scale, we convert numerical data to the sequence of events. Then, we define the notion of a compound event reflecting the basic unit of the pre-meal insulin therapy. The course of the disease is modeled as a sequence of compound events. To discover the patterns of such events, we propose a new algorithm based on the concept of frequent episodes. The patterns that were obtained are presented to physicians in form of a graph that illustrates the possible pathway of the therapy. Using the proposed approach, it is possible to model both mutual similarity and repetitiveness of the prescribed treatments. The proposed method was evaluated using the actual medical data of juvenile diabetic patients. We obtained encouraging results that have been evaluated positively by doctors.

Forecasting Daily Urban Water Demand Using Dynamic Gaussian Bayesian Network

The objective of the presented research is to create effective forecasting system for daily urban water demand. The addressed problem is crucial for cost-effective, sustainable management and optimization of water distribution systems. In this paper, a dynamic Gaussian Bayesian network (DGBN) predictive model is proposed to be applied for the forecasting of a hydrological time series. Different types of DGBNs are compared with respect to their structure and the corresponding effectiveness of prediction. First, it has been found that models based on the automatic learning of network structure are not the most effective, and they are outperformed by models with the designed structure. Second, this paper proposes a simple but effective structure of DGBN. The presented comparative experiments provide evidence for the superiority of the designed model, which outperforms not only other DGBNs but also other state-of-the-art forecasting models.

Fuzzy cognitive maps in the modeling of granular time series

Abstract In this study we propose a new approach to granular modeling of time series. In contrast to the existing fuzzy set-based models of time series, we engage information granules in time (granulation resulting in temporal segments). This method subsequently gives rise to information granules formed in the representation space of the series (in particular, the space of amplitude and space of changes of amplitude). Initially the time series is approximated as the sequence of granules forming a so-called granular time series (GTS). To develop a forecasting (prediction) model of the GTS, we cluster all information granules and regard the centers of the clusters obtained through fuzzy clustering as the concepts of the fuzzy cognitive map (FCM). We propose a matching mechanism to carry out description of the GTS and form the results as a vector of the concepts’ activations. In this way the GTS is represented as the sequence of vectors of the concepts’ activations, which is forecasted by the FCM. At the conceptual level, the forecasted granule is the FCM concept associated with the maximal degree of activation. At the numeric level, the predicted granule regarded as a fuzzy set is described in terms of its bounds and modal value. Experimental studies involving publicly available real-world data demonstrate the usefulness and satisfactory efficiency of the proposed approach.

A review on methods and software for fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.