ESTDD: Expert system for thyroid diseases diagnosis

Expert Systems with Applications Expert Systems with Applications 34 (2008) 242–246 www.elsevier.com/locate/eswa

ESTDD: Expert system for thyroid diseases diagnosis Ali Kelesß a

a,*

, Aytu¨rk Kelesß

b

Department of Computer Education and Instructional Technology, Faculty of Kazım Karabekir Education, Atatu¨rk University, Turkey b Faculty of Engineering, Atatu¨rk University, TR-25240 Erzurum, Turkey

Abstract Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. The thyroid gland is one of the most important organs in the body as thyroid hormones are responsible for controlling metabolism. As a result, thyroid function impacts on every essential organ in the body. When the thyroid produces too much hormone, the body uses energy faster than it should. This condition is called hyperthyroidism. When the thyroid does not produce enough hormone, the body uses energy slower than it should. This condition is called hypothyroidism. Thyroid disease can be difficult to diagnose because symptoms are easily confused with other conditions. When thyroid disease is caught early, treatment can control the disorder even before the onset of symptoms. This study aims at diagnosing thyroid diseases with a expert system that we called as a ESTDD (expert system for thyroid disease diagnosis). We found fuzzy rules by using neuro fuzzy method, which will be emplaced in ESTDD system. ESTDD could diagnose with 95.33% accuracy thyroid diseases. Beside it can be benefited from this system for training of students in medicine.  2006 Elsevier Ltd. All rights reserved. Keywords: Expert system; Neuro fuzzy; Thyroid diseases

1. Introduction The thyroid is a small gland, shaped like a butterfly, that rests in the middle of the lower neck. Its primary function is to control the body’s metabolism (rate at which cells perform duties essential to living). To control metabolism, the thyroid produces hormones, T4 and T3, which tell the body’s cells how much energy to use. The thyroid gland is one of the most important organs in the body as thyroid hormones are responsible for controlling metabolism. As a result, thyroid function impacts on every essential organ in the body. The seriousness of thyroid disorders should not be underestimated as thyroid storm (an episode of severe hyperthyroidism) and myx*

Corresponding author. Tel.: +90 442 2314518. E-mail address: [email protected] (A. Kelesß).

0957-4174/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.09.028

edema coma (the end stage of untreated hypothyroidism) may lead to death in a significant number of cases (Zhang & Berardi, 1998). A properly functioning thyroid will maintain the right amount of hormones needed to keep the body’s metabolism functioning at a satisfactory rate. As the hormones are used, the thyroid creates replacements. The quantity of thyroid hormones in the bloodstream is monitored and controlled by the pituitary gland. When the pituitary gland, which is located in the center of the skull below the brain, senses either a lack of thyroid hormones or a high level of thyroid hormones, it will adjust its own hormone (TSH) and send it to the thyroid to tell it what to do. When the thyroid produces too much hormone, the body uses energy faster than it should. This condition is called hyperthyroidism. When the thyroid does not produce enough hormone, the body uses energy slower than

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it should. This condition is called hypothyroidism. There are many different reasons why either of these conditions might develop. Currently, about 20 million Americans have some form of thyroid disease. People of all ages and races can get thyroid disease. However, women are five to eight times more likely than men to have thyroid problems. Thyroid disease can be difficult to diagnose because symptoms are easily confused with other conditions. When thyroid disease is caught early, treatment can control the disorder even before the onset of symptoms. Fortunately, there is a test, called the thyroid-stimulating hormone (TSH) test, that can identify thyroid disorders even before the onset of symptoms. The Journal of the American Medical Association found that screening for mild thyroid failure in women and men over age 35 is as cost-effective as screening for more common problems such as high cholesterol or high blood pressure. Proper interpretation of the thyroid data besides clinical examination and complementary investigation is an important issue in the diagnosis of thyroid disease. Various new methods, such as pattern recognition techniques, fuzzy classifiers, etc., have been used to fit patients into a well defined status. The used data in this study is the thyroid dataset taken from the UCI machine learning respiratory. The reason for using this dataset is that because it is very commonly used among the other classification systems that we have used to compare our system with for thyroid diagnosis problem. We found fuzzy rules by using neuro fuzzy method for this data set in which a more reliable result is found (95.33% accuracy) by 10-fold cross-validation method. If it is compared with classification results of other methods in literature, which is given in section of discussion, our result can be evaluated as a successful result. These fuzzy rules were used in inference engine of ESTDD by knowledge engineer with processing. This paper is organized as follows: the following second section gives the necessary background information about data set and used neuro fuzzy classification method. The third section is reserved for explanation of the developed expert system ESTDD. Then the fourth section discussions. Finally the last section five is the conclusion for this application.

2. Background 2.1. Thyroid dataset We used dataset for thyroid classification problem is taken from UCI machine learning respiratory. The data consist of the measurements of thyroid gland. This dataset contains three classes and 215 samples. These classes are assigned to the values that correspond to the hyper, hypo and normal function of the thyroid gland. All samples have five features. These are ():

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1. trd_1: T3-resin uptake test (A percentage). 2. trd_2: Total serum thyroxin as measured by the isotopic displacement method. 3. trd_3: Total serum triiodothyronine as measured by radioimmuno assay. 4. trd_4: Basal thyroid-stimulating hormone (TSH) as measured by radioimmuno assay. 5. trd_5: Maximal absolute difference of TSH value after injection of 200 mg of thyrotropin-releasing hormone as compared to the basal value. First class has 150 instances, second class has 35 instances and finally, third class contains 30 instances (). 2.2. Neuro fuzzy classification method Neuro fuzzy classification (NEFCLASS) algorithm is one of the tuning methods of the fuzzy systems based on adding and deleting the rules. The NEFCLASS algorithm was introduced by Nauck and his coworkers in 1994 (Nauck, Klawonn, & Kruse, 1997; Nauck & Kruse, 1995). The algorithm is based on a common multilayer perception structure whose weights are modeled by fuzzy sets and the activation, output and propagation functions are adapted accordingly. This approach preserves the common NN structure, but allows the interpretation of the resulting system by the associated fuzzy system. The shape and position of the membership functions are adapted iteratively during the learning procedure. More information about NEFCLASS can be found in Nauck and Kruse (1995). The NEFCLASS system has a 3-layer feed-forward architecture that is derived from a generic fuzzy perception (Nauck et al., 1997). The units in this network use t-norms or t-conorms as activation functions. The hidden layer represents fuzzy rules. Fuzzy sets are encoded as (fuzzy) connection weights. This view of a fuzzy system illustrates the data flow within the system (data and error signals), and its parallel nature. Neuro-fuzzy system as a special three layered feed-forward neural network where • the first layer represents the input variables that means the pattern tuples, • the hidden layer represents fuzzy rules, • the third layer represents the output variables that means one unit for every class, • the units use t-norms and t-conorms as activation functions, • the fuzzy sets are encoded as (fuzzy) connection weights. We investigated best model of classification for thyroid data set. As a result, the best model including five inputs, twenty rules and three outputs and raised accuracy of classification 95.33% (Fig. 1) was obtained by 10-fold crossvalidation method. Fuzzy rules of this model were used in ESTDD system as the inference engine (Fig. 2).

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Fig. 1. The neuro fuzzy model with five inputs, 20 rules and three outputs for ESTDD inference engine.

Fig. 3. Design of ESTDD system and its inference engine.

Fig. 2. Work diagram of ESTDD system.

3. Application of ESTDD system As this application is designed to be used by doctors, who are not advanced computer users, we aimed to implement the user interface of the thyroid diseases application as user friendly. The program has been implemented in Visual C# (microsoft visual studio 2005 developer tool) and SQL server 2005 was used as a database platform. ESTDD runs on windows environment (Fig. 2). We constructed a database for ESTDD system in which the detailed information of each patient would be kept. Patient number that is given to each patient for the differentiation, name and surname, medical values belongs to the patient, the doctor’s diagnosis field, the ESTDD’s diagnosis field and real diagnosis field is placed in database. Work diagram of ESTDD system was showed in Fig. 2. Fig. 3 shows how ESTDD system and its inference engine were designed. Process selection is main menu of ESTDD system (Fig. 4). This menu is realized a lot of process deal with data base, diagnose and education. We will talk about this process in followings. 3.1. Individually inquiry In the selection, diagnosis of thyroid diseases could realized by entering only medical data belong to one patient without recording the patient in database.

Fig. 4. Screen of process selection of ESTDD system.

3.2. Evaluation from file User is able to make evaluation of diagnosis by creating a text file including data belongs to patients. So, user can provide a batch evaluation without realizing database processes. 3.3. Data base evaluation Evaluation diagnosis can be made for all of patient recorded in ESTDD SQL database. Both list of patients in database and results list of diagnosis for those patients are appeared in this screen. In addition, statistical information deal with batch evaluation is given at the bottom of this screen.

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Fig. 5. Screen images of ESTDD system.

3.4. Data base and process Keeping the patient records, entrance of a new patient, searching for an already recorded patient or extracting a patient from the registration are some of the operations that leads to the construction of a database. All these operations are performed by specially prepared this selection. Records emplaced database are appeared at the bottom of this screen, too. So, user may control each process related to database and inquiry diagnosis, which will be selected for a patient in database. 3.5. PC and doctor diagnosis This process may be used as education aimed in diagnosis these diseases. Randomly selected medical data belongs to each patient is showed here and ESTDD system wants user to diagnose all patient or patients selected by educator. As a result ESTDD system compares user diagnosis with self diagnosis and real diagnosis if it is previously entered in system and this statistical evaluation is showed at the bottom of the screen. ESTDD system structure is extremely user friendly as it is easy to navigate with its wizard-like interface (Fig. 5).

gent programs does indeed offer significant benefits. One of the most important tasks now facing developers of AIbased systems is to characterize accurately those aspects of medical practice that are best suited to the introduction of artificial intelligence systems. Similar to other clinical diagnosis problems, classification systems have been used for thyroid disease diagnosis problem, too. Information obtained in literature deal with this subject was given in Table 1. It is perceived that various artificial intelligence methods have been used to diagnose thyroid diseases, when Table 1 were studied carefully. But there is not application designed as user friendly to be used by doctors or trained students in medicine. In recent years, ES’s have been used together with artificial neural networks, fuzzy logic, genetic algorithms and other methods of artificial intelligence. These methods allow taking into account their advantages in the designed system and, therefore, new designed systems are more powerful instruments to facilitate various tasks that require instant, accurate and reliable results. Our ESTDD system could be considered as one of these powerful instruments with the accuracy of 95.33%. 5. Conclusion

4. Discussion Much of the difficulty has been the poor way in which they have fitted into clinical practice, either solving problems that were not perceived to be an issue, or imposing changes in the way clinicians worked. What is now being realized is that when they fill an appropriately role, intelli-

Recent years have seen an enormous development in medical expert systems, and the systems now available are mature enough for targeted adoption in practice. In order to deliver health-care even more effectively, expert systems will be increasingly integrated in hospital information systems (HIS).

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Table 1 Published articles in the area of the use of AI method in thyroid diseases diagnosis (in thyroid data set taken from UCI machine learning respiratory) References and methods

Accuracy (%)

¨ zyılmaz and Yıldırım (2002) O MLP with bp (back-propagation) MLP with fbp (fast back-propagation) RBF CSFNN

86.33 (average-3-fold cross-validation) 89.80 (average-3-fold cross-validation) 79.08 91.138

Serpen et al. (1997) MLP LVQ RBF PPFNN

36.74 81.86 72.09 78.14

(test (test (test (test

data) data) data) data)

81.34 93.26 92.81 92.94 96.24 94.86

(test (test (test (test (test (test

data) data) data) data) data) data)

Pasi (2004) LDA C4.5-1 C4.5-2 C4.5-3 MLP DIMLP

References

In this study ESTDD (with NEFCLASS-J)

Thyroid disease can be difficult to diagnose because symptoms are easily confused with other condition. When thyroid disease is caught early, treatment can control the disorder even before the onset of symptoms. In this study, diagnosing thyroid disease is aimed with a expert system that is called as ESTDD (expert system for thyroid disease diagnosis). ESTDD is a tool, which is presented to endocrinologists or students. ESTDD could form a foresight diagnosis with 95.33% accuracy for thyroid diseases. Besides, this tool stores the patient records in database for further reference. In our opinion using this expert system in the education process provides a more colorful environment for the doctors than huge, hard-covered materials. Students studying endocrinology for thyroid diseases can use this system for testing their knowledge by comparing their predictions with ESTDD.

95.33 (10 fold cross-validation)

MLP with bp: multi layer perceptron with back-propagation. MLP with fbp: multi layer perceptron with fast back-propagation. RBF: radial basis function. CSFNN: adaptive conic section function neural network. LVQ: learning vector quantizer. PPFNN: probabilistic potential function neural network. LDA: linear discriminant analysis. C4.5-1: C4.5 with default learning parameters. C4.5-2: C4.5 with parameter c equal to 5. C4.5-3: C4.5 with parameter c equal to 95. DIMLP: DIMLP with two hidden layers and default learning parameters. NEFCLASS-J: neuro fuzzy classification Nauck (1999).

Nauck, D. (1999). Design and implementation of neuro-fuzzy data analysis tool in Java. Brauschweig: Technische Universita¨t Brauschweig. Nauck, D., & Kruse, R. (1995). NEFCLASS – A neuro fuzzy approach for the classification of data. In ACM symposium on applied computing (pp. 461–465). New York: ACM Press, Nashville, February 26–28. Nauck, D., Klawonn, F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. Chichester: Wiley. ¨ zyılmaz, L., & Yıldırım, T. (2002). Diagnosis of thyroid disease using O artificial neural network methods. In Proceedings of ICONIP’02 nineth international conference on neural information processing,Orchid Country Club, Singapore (pp. 2033–2036). Pasi, L. (2004). Similarity classifier applied to medical data sets, 2004, 10 sivua, Fuzziness in Finland’04. In International conference on soft computing, Helsinki, Finland & Gulf of Finland & Tallinn, Estonia. Serpen, G., Jiang, H. & Allred, L. (1997). Performance analysis of probabilistic potential function neural network classifier, In Proceedings of artificial neural networks in engineering conference, St. Louis, MO, Vol. 7 (pp. 471-476). Zhang, G., & Berardi, L. V. (1998). An investigation of neural networks in thyroid function diagnosis. Health Care Management Science, 29–37.