Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems

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1 Computers in Biology and Medicine 35 (25) Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems Elif Derya Ubeyl, İnan Guler Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 65 Teknikokullar, Ankara, Turkey Received 4 August 23; accepted 6 March 24 Abstract A newapproach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classiers learned howto dierentiate a newcase in the domain. The six ANFIS classiers were used to detect the six erythemato-squamous diseases when 34 features dening six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classier (combining ANFIS) was trained using the outputs of the six ANFIS classiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classication accuracies and the results conrmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.? 24 Elsevier Ltd. All rights reserved. Keywords: Adaptive neuro-fuzzy inference system (ANFIS); Fuzzy logic; Erythemato-squamous diseases detection. Introduction The dierential diagnosis of erythemato-squamous diseases is a dicult problem in dermatology. The diseases in this group are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris. They all share the clinical features of erythema and scaling Corresponding author. Tel.: ; fax: address: iguler@gazi.edu.tr (İ. Guler) /$ - see front matter? 24 Elsevier Ltd. All rights reserved. doi:.6/j.compbiomed

2 422 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) with very few dierences []. This is where fuzzy set theory plays an important role in dealing with uncertainty when making decisions in medical applications. Fuzzy sets have attracted the growing attention and interest in modern information technology, production technique, decision making, pattern recognition, diagnostics, data analysis, etc. [2 4]. Neuro-fuzzy systems are fuzzy systems which use articial neural networks (ANNs) theory in order to determine their properties (fuzzy sets and fuzzy rules) by processing data samples. Neuro-fuzzy systems harness the power of the two paradigms: fuzzy logic and ANNs, by utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way man processes information. A specic approach in neuro-fuzzy development is the adaptive neuro-fuzzy inference system (ANFIS), which has shown signicant results in modelling nonlinear functions. In ANFIS, the membership function parameters are extracted from a data set that describes the system behavior. The ANFIS learns features in the data set and adjusts the system parameters according to a given error criterion [5,6]. Successful implementations of ANFIS in biomedical engineering have been reported for classication [7,8] and data analysis [9]. In this study, a newapproach based on ANFIS was presented for the detection of erythematosquamous diseases. The six ANFIS classiers were used to detect the six erythemato-squamous diseases when 34 features dening six disease indications were used as inputs. Each of the ANFIS classier was trained so that they are likely to be more accurate for one type of erythemato-squamous disease than the other diseases. The predictions of the six ANFIS classiers were combined by the seventh ANFIS classier. The dermatology database investigated in this study consisted of 358 cases of erythemato-squamous diseases compiled by Guvenir et al. []. The proposed ANFIS model was then evaluated and performances of the ANFIS model were reported. We were able to achieve signicant improvement in accuracy by applying ANFIS model compared to the stand-alone neural networks. Finally, some conclusions were drawn concerning the impacts of features on the detection of erythemato-squamous diseases. 2. Dierential diagnosis of erythemato-squamous diseases The erythemato-squamous diseases are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris. These diseases are frequently seen in the outpatient dermatology departments. Since they all share the clinical features of erythema and scaling with slight variations, the dierential diagnosis of erythemato-squamous diseases is dicult. At rst sight, all the diseases look very much alike with the erythema and scaling. When inspected more carefully, some patients have the typical clinical features of the disease at the predilection sites (localizations of the skin which a disease prefers) while another group has typical localizations. Patients were rst evaluated clinically with 2 features. The degree of erythema and scaling, whether the borders of lesions are denite or not, the presence of itching and koebner phenomenon, the formation of papules, whether the oral mucosa, elbows, knees and the scalp are involved or not, whether there is a family history or not, are all important in dierential diagnosis. The erythema and scaling of chronic dermatitis is less than that of psoriasis, the koebner phenomenon is present only in psoriasis, lichen planus and pityriasis rosea. Itching and polygonal papules are for lichen planus, whereas follicular papules are for pityriasis rubra pilaris. Oral mucosa is a predilection site for lichen

3 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) planus whilst knee, elbow and scalp involvements are for psoriasis. Family history is usually present for psoriasis and pityriasis rubra pilaris usually starts during childhood. Some patients can be diagnosed with these clinical features only, however, a biopsy is usually necessary for a correct and denite diagnosis. Skin samples were taken for the evaluation of 22 histopathological features. Another diculty for dierential diagnosis is that a disease may showthe histopathological features of another disease at the beginning stage and may have the characteristic features at the following stages. Some samples show the typical histopathological features of the disease while some do not. Melanin incontinence is a diagnostic feature for lichen planus, brosis of the papillary dermis is for chronic dermatitis, exocytosis may be seen in lichen planus, pityriasis rosea and seboreic dermatitis. Acanthosis and parakeratosis can be seen in all of the diseases at dierent levels. Clubbing of the rete ridges, thinning of the suprapapillary epidermis are diagnostic for psoriasis. The disappearance of the granular layer, vacuolization and damage of basal layer, saw-tooth appearance of retes and a band-like inltrate are diagnostic for lichen planus. Follicular horn plug and perifollicular parakeratosis are hints for pityriasis rubra pilaris []. 3. Adaptive neuro-fuzzy inference system (ANFIS) 3.. Architecture of ANFIS The ANFIS is a fuzzy Sugeno model put in the framework of adaptive systems to facilitate learning and adaptation [5,6]. Such framework makes the ANFIS modeling more systematic and less reliant on expert knowledge. To present the ANFIS architecture, two fuzzy if then rules based on a rst order Sugeno model are considered Rule : If (x is A ) and (y is B ) then (f = p x + q y + r ). Rule 2: If (x is A 2 ) and (y is B 2 ) then (f 2 = p 2 x + q 2 y + r 2 ), where x and y are the inputs, A i and B i are the fuzzy sets, f i are the outputs within the fuzzy region specied by the fuzzy rule, p i ; q i and r i are the design parameters that are determined during the training process. The ANFIS architecture to implement these two rules is shown in Fig., in which a circle indicates a xed node, whereas a square indicates an adaptive node. In the rst layer, all the nodes are adaptive nodes. The outputs of layer are the fuzzy membership grade of the inputs, which are given by O i = Ai (x); i=; 2; O i = Bi 2 (y); i=3; 4; () (2) where Ai (x); Bi 2 (y) can adopt any fuzzy membership function. For example, if the bell shaped membership function is employed, Ai (x) is given by Ai (x)= +{((x c i )=a i ) 2 } bi ; where a i ; b i and c i are the parameters of the membership function, governing the bell-shaped functions, accordingly. (3)

4 424 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) Layer Layer 2 Layer 3 Layer 4 Layer 5 x y x A M w N w w f A 2 S f B y M N w 2 f 2 B 2 w 2 w 2 x y Fig.. ANFIS architecture. In the second layer, the nodes are xed nodes. They are labeled with M, indicating that they perform as a simple multiplier. The outputs of this layer can be represented as Oi 2 = w i = Ai (x) Bi (y); i=; 2; (4) which are the so-called ring strengths of the rules. In the third layer, the nodes are also xed nodes. They are labeled with N, indicating that they play a normalization role to the ring strengths from the previous layer. The outputs of this layer can be represented as Oi 3 =w i = w i ; i=; 2; (5) w + w 2 which are the so-called normalized ring strengths. In the fourth layer, the nodes are adaptive nodes. The output of each node in this layer is simply the product of the normalized ring strength and a rst-order polynomial (for a rst-order Sugeno model). Thus, the outputs of this layer are given by Oi 4 =w i f i =w i (p i x + q i y + r i ); i=; 2: (6) In the fth layer, there is only one single xed node labeled with S. This node performs the summation of all incoming signals. Hence, the overall output of the model is given by 2 Oi 5 = w i f i = ( 2 i= w if i ) : (7) w + w 2 i= It can be observed that there are two adaptive layers in this ANFIS architecture, namely the rst layer and the fourth layer. In the rst layer, there are three modiable parameters {a i ;b i ;c i }, which are related to the input membership functions. These parameters are the so-called premise parameters. In the fourth layer, there are also three modiable parameters {p i ;q i ;r i }, pertaining to the rst order polynomial. These parameters are so-called consequent parameters [5,6] Learning algorithm of ANFIS The task of the learning algorithm for this architecture is to tune all the modiable parameters, namely {a i ;b i ;c i } and {p i ;q i ;r i }, to make the ANFIS output match the training data. When the

5 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) premise parameters a i ; b i and c i of the membership function are xed, the output of the ANFIS model can be written as f = w f + w 2 f 2 : w + w 2 w + w 2 Substituting Eq. (5) into Eq. (8) yields f =w f +w 2 f 2 : (8) (9) By substituting the fuzzy if then rules into Eq. (9), it becomes f =w (p x + q y + r )+ w 2 (p 2 x + q 2 y + r 2 ): () After rearrangement, the output can be expressed as f =(w x)p +(w y)q +(w )r +(w 2 x)p 2 +(w 2 y)q 2 +(w 2 )r 2 ; () which is a linear combination of the modiable consequent parameters p ; q ; r ; p 2 ; q 2 and r 2. The least squares method can be used to identify the optimal values of these parameters easily. When the premise parameters are not xed, the search space becomes larger and the convergence of the training becomes slower. A hybrid algorithm combining the least squares method and the gradient descent method is adopted to solve this problem. The hybrid algorithm is composed of a forward pass and a backward pass. The least squares method (forward pass) is used to optimize the consequent parameters with the premise parameters xed. Once the optimal consequent parameters are found, the backward pass starts immediately. The gradient descent method (backward pass) is used to adjust optimally the premise parameters corresponding to the fuzzy sets in the input domain. The output of the ANFIS is calculated by employing the consequent parameters found in the forward pass. The output error is used to adapt the premise parameters by means of a standard backpropagation algorithm. It has been proven that this hybrid algorithm is highly ecient in training the ANFIS [5,6]. In this study, a newapproach based on ANFIS was presented for the detection of erythematosquamous diseases. The six ANFIS classiers were used to detect the six erythemato-squamous diseases when 34 features dening six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classier (combining ANFIS) was trained using the outputs of the six ANFIS classiers as input data. The dermatology database investigated in this study consisted of 358 cases of erythemato-squamous diseases compiled by Guvenir et al. []. 4. Results and discussion The collection of well-distributed, sucient, and accurately measured input data is the basic requirement to obtain an accurate model. In the data set, the family history feature has a value of if any of the erythemato-squamous diseases have been observed in the family, otherwise it has a value of zero. The age feature simply represents the age of the patient. Every other feature (clinical and histopathological) was given a degree in the range 3. Here, indicates that the feature was not present, 3 indicates the largest amount possible, whilst, 2 indicate the relative intermediate

6 426 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) Table The data set used in the present study Erythemato-squamous Features diseases Clinical Histopathological (number of patients) Psoriasis () Feature : erythema Feature 2: melanin incontinence Seboreic dermatitis (6) Feature 2: scaling Feature 3: eosinophils in the inltrate Lichen planus (7) Feature 3: denite borders Feature 4: PNL inltrate Pityriasis rosea (48) Feature 4: itching Feature 5: brosis of the papillary dermis Chronic dermatitis (48) Feature 5: koebner phenomenon Feature 6: exocytosis Pityriasis rubra pilaris (2) Feature 6: polygonal papules Feature 7: acanthosis Feature 7: follicular papules Feature 8: hyperkeratosis Feature 8: oral mucosal involvement Feature 9: parakeratosis Feature 9: knee and elbowfeature 2: clubbing of the rete ridges involvement Feature : scalp involvement Feature 2: elongation of the rete ridges Feature : family history Feature 22: thinning of the suprapapillary epidermis Feature 34: age Feature 23: pongiform pustule Feature 24: munro microabcess Feature 25: focal hypergranulosis Feature 26: disappearance of the granular layer Feature 27: vacuolization and damage of basal layer Feature 28: spongiosis Feature 29: saw-tooth appearance of retes Feature 3: follicular horn plug Feature 3: perifollicular parakeratosis Feature 32: inammatory mononuclear inltrate Feature 33: band-like inltrate values. The data set consisting of 34 features that used in the present study and the six classes of erythemato-squamous diseases are summarized in Table. The proposed technique involved training the six ANFIS classiers to detect the six classes of erythemato-squamous diseases when the features given in Table were used as inputs. We trained the six ANFIS classiers since there were six possible outcomes of the diagnosis of erythemato-squamous diseases (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris). Each of the ANFIS classier was trained so that they are likely to be more accurate for one type of erythemato-squamous disease than the other diseases. For detection of the six classes of erythemato-squamous diseases, the data set consisting of 34 features of 358 patients were presented to each of the ANFIS classier. Samples with target outputs psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris were given the binary target values of (,,,,,), (,,,,,), (,,,,,), (,,,,,), (,,,,,), and (,,,,,), respectively.

7 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) input (3) input2 (3) anfis (sugeno) f(u) input3 (3) 562 rules output (562) input34 (3) Fig. 2. Fuzzy rule architecture of the six ANFIS classiers. System ANFIS: 34 inputs, output, 562 rules. We trained the seventh ANFIS classier to combine the predictions of the six ANFIS classiers. The outputs of the six ANFIS classiers were used as the inputs of the seventh ANFIS classier. Fig. 2 shows the fuzzy rule architecture of the six ANFIS classiers using a generalized bell shaped membership function dened in Eq. (3). There are a total of 562 fuzzy rules in the architecture. Fig. 3 shows the fuzzy rule architecture of the combining ANFIS classier (the seventh ANFIS classier) using a generalized bell shaped membership function. There are a total of 54 fuzzy rules in the architecture. Each ANFIS shown in Figs. 2 and 3 was implemented by using MATLAB software package (MATLAB version 6. with fuzzy logic toolbox). The data set (358 patients) was divided into two separate data sets the training data set and the testing data set. The training data set was used to train ANFIS model, whereas the testing data set was used to verify the accuracy and the eectiveness of the trained ANFIS model for the detection of the six classes of erythemato-squamous diseases. Each ANFIS shown in Figs. 2 and 3 used 79 training data in 5 training periods and the step size for parameter adaptation had an initial value of.. The steps of parameter adaptation of each ANFIS are shown in Fig. 4. At the end of 5 training periods, the network error convergence curve of each ANFIS was derived as shown in Fig. 5. From the curve, the nal convergence value is 5:36 6. In a real-world domain, just like the one used in the present study, all of the features used in the descriptions of instances may have dierent levels of relevancy. Therefore, in the present study changes of the nal (after training) generalized bell shaped membership functions with respect to the initial (before training) generalized bell shaped membership functions of the input parameters were examined. Membership function of each input parameter was divided into three regions, namely, small, medium, and large. Fig. 6 shows the initial and nal generalized bell shaped membership

8 428 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) input (3) input2 (3) anfis (sugeno) f(u) input3 (3) 54 rules output (54) input6 (3) Fig. 3. Fuzzy rule architecture of the combining ANFIS (the seventh ANFIS classier). System ANFIS: 6 inputs, output, 54 rules Step size Epoch Fig. 4. Adaptation of parameter steps of each ANFIS. functions of the two input parameters (inputs 5 and 32) having the greatest impact and the two input parameters (inputs 7 and 3) having the least impact on the detection of erythemato-squamous diseases. Fig. 6(a) shows the initial generalized bell-shaped membership functions of the input 5 (koebner phenomenon), input 7 (acanthosis), input 3 (follicular horn plug) and input 32 (inammatory mononuclear inltrate). Fig. 6(b) shows the considerable change in the nal membership function of koebner phenomenon (input 5) after training. Fig. 6(c) shows the nal membership

9 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) x -3.8 Mean square error Epoch Fig. 5. The curve of network error convergence of each ANFIS. function of the inammatory mononuclear inltrate (input 32) and it also shows a distinctive change in the nal membership function of input 32. Fig. 6(d) shows the little change in the nal membership function of acanthosis (input 7). Fig. 6(e) shows the nal membership function of follicular horn plug (input 3) and it also shows a slight change in the nal membership function of input 3. The examination of initial and nal membership functions indicates that there is a considerable change in the nal membership function of koebner phenomenon (input 5), followed by inammatory mononuclear inltrate (input 32) and that changes in nal membership function of acanthosis (input 7) is the smallest, followed by follicular horn plug (input 3). This analysis was done since the amount of changes in the nal membership functions of inputs indicates the impact of inputs on the detection of output. By the analysis of membership functions the impact of inputs on the detection of erythemato-squamous diseases was determined. Based on the analysis of membership functions, it can be mentioned that koebner phenomenon has the greatest impact on the detection of erythemato-squamous diseases, followed by inammatory mononuclear inltrate and that acanthosis has the least impact, followed by follicular horn plug. After training, 79 testing data were used to validate the accuracy of the ANFIS model for the detection of erythemato-squamous diseases. All of the test cases had one of the six erythemato-squamous diseases. In classication, the aim is to assign the input patterns to one of several classes, usually represented by outputs restricted to lie in the range from to, so that they represent the probability of class membership. While the classication is carried out, a specic pattern is assigned to a specic class according to the characteristic features selected for it. In this application, there were six classes: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. Classication results of the ANFIS model were displayed by a confusion matrix. The confusion matrix is dened by labeling the desired classication on the rows and the actual network outputs on the columns. The confusion matrix showing the classication results of the ANFIS model is given below.

10 43 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) inmf inmf2 inmf3 in5mf in5mf2 in5mf3.8.8 Degree of membership Degree of membership (a) input 5, input 7, input 3,input32 (b) input 5 in32mf in32mf2 in32mf3 in7mf in7mf2 in7mf3.8.8 Degree of membership Degree of membership (c) input 32 (d) input 7 3 in3mf in3mf2 in3mf3.8 Degree of membership (e) input 3 Fig. 6. (a) Initial generalized bell-shaped membership functions of inputs 5, 7, 3, 32; (b) nal generalized bell-shaped membership function of input 5; (c) nal generalized bell shaped membership function of input 32; (d) nal generalized bell-shaped membership function of input 7; (e) nal generalized bell shaped membership function of input 3.

11 Confusion matrix: E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) Desired result Output result Psoriasis Seboreic Lichen Pityriasis Chronic Pityriasis dermatitis planus rosea dermatitis rubra pilaris Psoriasis 54 Seboreic dermatitis 29 Lichen planus 34 Pityriasis rosea 22 Chronic dermatitis 23 Pityriasis rubra pilaris 9 According to the confusion matrix, subject suering from psoriasis was classied incorrectly by the ANFIS model as a subject suering from seboreic dermatitis, subject suering from psoriasis was classied as a subject suering from pityriasis rosea, subject suering from seboreic dermatitis was classied as a subject suering from psoriasis, subject suering from lichen planus was classied as a subject suering from pityriasis rosea, subject suering from pityriasis rosea was classied as a subject suering from seboreic dermatitis, subject suering from pityriasis rosea was classied as a subject suering from chronic dermatitis, subject suering from chronic dermatitis was classied as a subject suering from psoriasis and subject suering from pityriasis rubra pilaris was classied as a subject suering from pityriasis rosea. The test performance of the ANFIS model was determined by the computation of the statistical parameters such as sensitivity, specicity and total classication accuracy. Binomial condence intervals (CIs) were calculated for sensitivity and specicity values []. The sensitivity, specicity and total classication accuracy are dened as follows: Sensitivity: number of true positive decisions/number of actually positive cases. Specicity: number of true negative decisions/number of actually negative cases. Total classication accuracy: number of correct decisions/total number of cases. A true positive decision occurs when the positive detection of the network coincided with a positive detection of the physician. A true negative decision occurs when both the network and the physician suggested the absence of a positive detection. The subjects suering from psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris were classied with the accuracy of 95.5% (total classication accuracy). The values of sensitivity and 95% CIs of sensitivity values for the six erythematosquamous diseases are given in Table 2. The values of specicity and 95% CIs of specicity values for the six erythemato-squamous diseases are given in Table 3. As it is seen from Table 2, the ANFIS model classied subjects suering from psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris with the accuracy of 96.4%, 96.7%, 97.%, 9.7%, 95.8%, and 9.%, respectively. The correct classication rates of the stand-alone neural network (multilayer perceptron neural network) were 85.7% for subjects suering from psoriasis, 86.7% for subjects suering from seboreic dermatitis, 85.7% for subjects suering from lichen planus, 83.3% for subjects suering from pityriasis rosea, 87.5% for subjects suering from chronic

12 432 E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) Table 2 The values of sensitivity and 95% CIs of sensitivity values for the six erythemato-squamous diseases Erythemato-squamous diseases Sensitivity (%) 95% CIs (%) Psoriasis Seboreic dermatitis Lichen planus Pityriasis rosea Chronic dermatitis Pityriasis rubra pilaris Table 3 The values of specicity and 95% CIs of specicity values for the six erythemato-squamous diseases Erythemato-squamous diseases Specicity (%) 95% CIs (%) Psoriasis Seboreic dermatitis Lichen planus Pityriasis rosea Chronic dermatitis Pityriasis rubra pilaris dermatitis and 8.% for subjects suering from pityriasis rubra pilaris. The total classication accuracy of the stand-alone neural network was 85.5%. Thus, the accuracy rates of the ANFIS model presented for this application were found to be higher than that of the stand-alone neural network model. These results indicate that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. 5. Conclusion This paper presented a newapplication of ANFIS model for the detection of erythemato-squamous diseases. We chose fuzzy logic in the present study due to the uncertainty in dierential diagnosis of erythemato-squamous diseases, which is a result of imprecise boundaries among the six erythemato-squamous diseases. Using fuzzy logic enabled us to use this uncertainty in the classi- ers designs and consequently to increase the credibility of the systems outputs. The six ANFIS classiers were used to detect the six erythemato-squamous diseases when 34 features dening six disease indications were used as inputs. The predictions of the six ANFIS classiers were combined by the seventh ANFIS classier. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The total classication accuracy of the ANFIS model was 95.5%. We, therefore, have concluded that the proposed ANFIS model can be used in detecting erythemato-squamous diseases by taking into consideration the misclassication rates.

13 Acknowledgements E.D. Ubeyl, İ Guler / Computers in Biology and Medicine 35 (25) This study has been supported by the State Planning Organization of Turkey (Project No: 23K 247, Project name: Biomedical signal acquisition, processing and imaging) References [] H.A. Guvenir, G. Demiroz, N. İlter, Learning dierential diagnosis of erythemato-squamous diseases using voting feature intervals, Artif. Intell. Med. 3 (998) [2] D. Dubois, H. Prade, An introduction to fuzzy systems, Clin. Chim. Acta 27 (998) [3] L.I. Kuncheva, F. Steimann, Fuzzy diagnosis, Artif. Intell. Med. 6 (999) [4] D. Nauck, R. Kruse, Obtaining interpretable fuzzy classication rules from medical data, Artif. Intell. Med. 6 (999) [5] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (3) (993) [6] J.-S.R. Jang, Self-learning fuzzy controllers based on temporal backpropagation, IEEE Trans. Neural Networks 3 (5) (992) [7] J. Usher, D. Campbell, J. Vohra, J. Cameron, A fuzzy logic-controlled classier for use in implantable cardioverter debrillators, Pace-Pacing Clin. Electrophysiol. 22 (999) [8] S.Y. Belal, A.F.G. Taktak, A.J. Nevill, S.A. Spencer, D. Roden, S. Bevan, Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system, Artif. Intell. Med. 24 (22) [9] I. Virant-Klun, J. Virant, Fuzzy logic alternative for analysis in the biomedical sciences, Comput. Biomed. Res. 32 (999) [] P. Armitage, G. Berry, Statistical Methods in Medical Research, Blackwell Science, Oxford, 994. İnan Guler graduated from Erciyes University in 98. He took his M.S. Degree from Middle East Technical University in 985, and his Ph.D. Degree from İstanbul Technical University in 99, all in Electronic Engineering. He is a professor at Gazi University where he is Head of Department. His areas of interest include biomedical systems, biomedical signal processing, biomedical instrumentation, electronic circuit design, neural network, and articial intelligence. He has written more than articles on biomedical engineering. Elif Derya Ubeyl graduated from C ukurova University in 996. She took her M.S. Degree in 998, all in electronic engineering. She took her Ph.D. Degree from Gazi University, electronics and computer technology. She is a research assistant at the Department of Electronics and Computer Education at Gazi University. Her areas of interest are biomedical signal processing, neural network, and articial intelligence.