Liver Disease Diagnosis Based on Neural Networks

Transcription

1 Liver Disease Diagnosis Based on Neural Networks 1 EBENEZER OBALOLUWA OLANIYI, 2 KHASHMAN ADNAN 1 Near East University, via Mersin 10, Lefkosa, Turkey, 1 Member, Centre of Innovation for Artificial Intelligence 2 Founder and Director Centre of Innovation for Artificial Intelligence 2 British University of Nicosia Girne, via Mersin 10 Turkey Obalolu117@yahoo.com adnan.khashman@bun.edu.tr Abstract: - In this paper, two models of artificial neural network have been developed to solve the problems facing physicians in diagnosis of liver diseases. Experience has shown that many patients suffering from liver disorder die daily as a result of misdiagnosis of the diseases. Therefore, two models: back propagation neural network and radial basis function neural network are designed to diagnose these diseases and also prevent misdiagnosis of the liver disorder patients. These systems are developed using the BUPA liver disorder dataset obtained from UCI machine learning repository. The dataset is made up of 6 attributes which are major factors that cause liver disorder in patients. The results obtained from testing of the networks were compared with each other. Also, with the previous research on liver disorder using the same dataset to ascertain the best network needed for diagnosis of the disease. Key-Words: - Liver disorder, BUPA dataset, Radial basis function, Back propagation neural network, Data mining, machine learning 1 Introduction The liver is the largest internal organ in the body with approximately 4% of the body weight and blood flow 1.5 litre per minute [1]. It receives blood supply from two major blood vessels: the hepatic artery and the portal vein. The hepatic artery supplies oxygenated blood, where as portal vein which provides 80% of the total blood supply, supplies nutrient rich deoxygenated blood [2].The normal pressure in the portal vein is between 3 and 5mmHg. The liver is located at the right upper quadrant of the abdomen, completely protected by the thoracic rib cages. The liver served informed of a guard between the digestive tract and other part of the body. The liver detoxify, accumulates metabolites. In addition, the liver has the capability of producing plasma protein for example albumin which are delivering into the blood likewise metabolites which are constituent of the bile [2]. Liver diseases can be described as any defects that affect the liver. The types of liver diseases can be categories into hepatocellular (hepatitis, heart failure and toxins), cholestatic, infiltrative diseases (tumor, sarcoid) and cirrhosis (hepatocellular loss and scarring). The major factors that have resulted into this increase in number of liver disease patient are alcohol, viral hepatitis and obesity leading to nonalcoholic fatty liver disease. In the United Kingdom, liver disease was once thought as an uncommon disease but nowadays its rated as the fifth common cause of death [3][4]. Misdiagnoses of the patients have added to the rate of death recorded from liver diseases all over the world. Therefore, developing a machine that will enhance in the diagnosis of the disease will be of a great advantage in the medical field. These systems will help the physicians in making accurate decisions on patients whether liver diseases are present or not. Therefore, in this research work, the artificial network have been developed as generalization of mathematical models of human cognition or neural biology [5],[6]. Two models of neural network have been developed. These models are feed forward neural networks trained with back propagation and radial basis function neural networks. The two models are three networks which are made up of an input that is connected with the hidden with aid of connection weight. The hidden is also connected with the output also with the aid of connection weight. The different between the two networks is the kind of an activation function that is present in the neurons of the hidden and the neurons in the output of the two networks. In the feed forward neural network, sigmoid activation function is present in the hidden and the output because of its soft switching ability and also simplicity in its derivative. In the second ISBN:

2 network the radial basis function neural network has Gaussian function present at the hidden and linear function at its output. Radial basis function neural network can also be described as the locally exponential damped non linear function, locally approximating to non linear input or output map. This means that radial basis function required less parameter in shorter training to the same accuracy. The rest of the paper is arranged as followed: section 2 is the related work, section 3 is the material and method, section 4 is the design of the network, section 5 is the result evaluation, and section 6 is the conclusion. 2 Related works In recent research works, several neural network models have been developed to aid in diagnosis of liver diseases in the medical field by the physicians such as diagnosis support system [7], expert system [8], intelligent diagnosis system [9], and hybrid intelligent system. In addition, Christopher et. al 10] proposed a system to diagnose medical diseases considering 6 benchmarks which are liver disorder, heart diseases, diabetes, breast cancer, hepatitis and lymph. The authors developed two systems based on WSO and C4.5, an accuracy of 64.60% with 19 rules of liver disorder dataset and 62.89% with 43 rules which was obtained from the WSO and C4.5 respectively. Ramana et al[11][12]also made a critical study on liver diseases diagnosis by evaluating some selected classification algorithms such as naïve Bayes classifier, C4.5, backpropagation neural network, K-NN and support vector. The authors obtained 51.59% accuracy on Naïve Bayes classifier, 55.94% on C4.5 algorithm, 66.66% on BPNN, 62.6% on KNN and 62.6% accuracy on support vector machine. The poor performance in the training and testing of the liver disorder dataset as resulted from an insufficient in the dataset. Therefore, Sug [12], suggested a method based on oversampling in minor classes in order to compensate for the insufficiency of data effectively. The author considered two algorithms of decision tree for the research work. These algorithms are C4.5 and CART and the dataset of BUPA liver disorder was also considered for the experiments. These previously designed systems have been adequate but more works has to be done on their recognition rate for better accuracy in the diagnosis of the liver disease. In this case, this will make the diagnoses of the liver diseases to be more effective and efficient by preventing misdiagnosis of the liver disorder. Developing a system with better performance than the previous works will help in preventing misdiagnosis of the disease and help in providing the best and required medication for the patient. 3 Material and Method The liver dataset is obtained from UCI machine learning repository created by BUPA Medical research limited, India [13]. The dataset is made up of six (6) attributes in which first five attributes are blood test which were thought to be sensitive to liver disorders that might arise from excessive consumption of alcohol. The following are the attributes with their corresponding description that were published. Table1: The description of the attributes in liver disorder dataset [13] Attributes Description Mcv Mean corpuscular volume Alkphos Alkaline phosphotase Sgpt Alamine aminotransferase Sgot Aspartate aminotransferase Gammagt Gamma-glutamyl transpeptidase Drinks Number of half-pint equivalent of alcoholic beverages drunk per day Selector Presence or absence of liver disorder 3.1 Preprocessing In order for the classifier to produce the best performance, there is a need to transform the attributes values into homogenous and well behaved values that yield numerical stability [14]. Therefore, the attributes values have to be in value ranging between 0 and 1. The process is referred to as normalization. Normalization is done by dividing each sample of a particular attribute by its maximum value. ISBN:

3 Normalized Value = Sample of an attribute Maximum value of the attribute Table 2 shows the maximum value of the sample of the attributes of liver disorder dataset Table 2: The maximum value of sample of liver disorder Attributes Maximum sample value Mcv 103 Alkphos 138 Sgpt 155 Sgot 82 Gammagt 297 Drinks 20 (1) disorder absence (0, 1). The numbers of neurons needed at the hidden are experimenting in order to determine the best neurons that can represent the features present in the input dataset accurately to produce the required optimum result. The numbers of neurons required in the hidden were experimenting by varying the neurons starting from three (3) neurons until 10 neurons were reached which represent the patterns in the dataset accurately. Sigmoid function was used as the activation function both in the hidden neuron and the output neuron. The sigmoid function was used because of its soft switching ability and simplicity in derivatives. After the normalization has been carried out. The result obtained is value that range between 0 and 1. The table 3 shows the normalized value for the first five samples of the dataset. Table3: The normalized sample of the liver disorder Attrib Normalized samples utes Mcv Alkph os Sgpt Sgot Gamm agt Drinks Design of the network Two models are designed to diagnose the liver disorder in the medical field. The first model is the backpropagation neural network and the second model is radial basis function neural network. In the backpropagation neural network, six (6) input neuron was present at the input, this represents the number of attributes that cause the liver disorder. The output contains two neurons which denote either liver disorder present (1, 0) or liver Fig. 1 Design of the backpropagation neural network The second model; radial basis function neural network also has three s; the input is the non processing where the data is introduced into the network. The input is made up of 6 attributes which denotes causes of the liver disorder in patient. The output is a process which determines the output of the network. It also made of two neurons which represent if liver disorder is present (1, 0) or liver disorder absent (0, 1). The output neurons are made up of a linear activation function. The hidden is made up of radial basis function neural network neurons and bias. Each neuron in the hidden is made up of a non linear ISBN:

4 activation function for the input data. The most often used radial basis function is called Gaussian function. Gaussian function is characterized by a centre (c j ) and width (r j ). To calculate the radial basis function, the Euclidean distance input(x) and the radial basis function center (c j ) have to be measured. Also, the nonlinear transformation has to be performed with the radial basis function in the hidden as shown in Equation 2 and the structural representation of the neural network is shown in Fig 2. h j (x) = exp(-//x c j // 2 /r j 2 ) (2) Input Hidden Output Fig. 2 Design of the Radial basis neural network 5 Result Evaluation In order to obtain a required recognition rate that is capable enough to diagnose the liver disorder in a patient. There is a need for varying certain parameters in the neural network models to produce the required optimum result. These parameters are the learning rate, momentum rate and the hidden neurons. All these parameters present in the backpropagation neural networks. The learning rate is the learning power of the system, the momentum rate determine the learning speed of the system. The number of hidden neurons in the network has to be varied to produce the optimal result. The hidden neuron was varied from four (4) to ten (10) which produced the optimal result. The learning rate and the momentum rate that was finally used for this research work was 0.27 and 0.77 respectively. The recognition rate of 63% was obtained from the testing of the neural network. In the radial basis function, the spread constant of 5.3 at the hidden neuron of 200 produce the optimal recognition rate of 70%. Table 4: The performance table for the two models Backpropagation Neural Radial Basis Network Neural Network No of Input 6 No of Input 6 No of hidden 10 No of 200 hidden No of Output 2 No of Output Learning rate 0.27 Spread 5.3 constant Momentum rate 0.77 Epoch 200 Recognition rate 63% Recognition rate 70% 2 ISBN:

5 The result that was obtained using backpropagation neural network and radial basis neural network were compared with the previous works to ascertain the best network. The comparison is shown in Table 5. Table 5: The comparison table with previous works C % CART 65.32% Naïve Bayes 51.59% SVM 62.6% BPNN 63% RBFN 70% From Table 5, the radial basis function neural network has been discovered to have the optimal recognition rate which makes it to be more effective for the diagnosis of liver disorder compared with other recent algorithms. 6 Conclusion Two models have been developed to diagnose liver disorder in a patient. It has been discovered that the radial basis function neural network has the optimal recognition rate in diagnoses of the liver disorder. The radial basis function neural network has a recognition rate of 70% which has proved more accurate and efficient than the other algorithms. Comparing this work with the previous research works, it was discovered that the CART algorithm also proved efficient. But due to the significant of this work in the medical field. It should be noted that the RBFNN is the optimal model because of its higher recognition rate compared to other previous work. 7 Acknowledgements The authors would like to thank Mr. Rakan Khashman (LLB) for proof reading this paper. References: [1] A. N. Edginton and L. Ritter, Predicting Plasma Concentrations of Bisphenol A in Children Younger Than 2 Years of Age after Typical Feeding Schedules, using a Physiologically Based Toxicokinetic Model, Environmental Health Perspectives vol. 117 number 4, 2009, pp [2] K. Vekemans, F. Braet, Structural and functional aspects of the liver and liver sinusoidal cells in relation to colon carcinoma metastasis, World Journal of Gastroenterology, 2005;11(33): [3] M. Lombard, M. Clayton, Caring for people with liver disease: a competence framework for nursing, the Royal College of Nursing 20 Cavendish Square London W1G 0RN, [4] National Plan for Liver Services UK (2009) A Timeto Act: Improving Liver Health and Outcomes in Liver disease [5] S. Ansari I. Shafi J. Ahmad S. Ismail Shah, Neural network-based approach for the noninvasive diagnosis and classification of hepatotropic viral disease, Communications, IET, Vol. 6, 2012, [6] L. Fausett, Fundamentals of neural networks architectures, algorithms, and applications, Pearson Education, Inc., [7] N. Douali, H. Csaba, J. De Roo, E.I. Papageorgiou, M.-C.Jaulent, Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks, Comput. Methods Programs Biomed. 113, 2014, [8] H. Yan, Y. Jiang, J. Zheng, B. Fu, S. Xiao, C. Peng, The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems, Comput. Methods Programs Biomed. 74, 2004, [9] P. T. Karule, S. V. Dudul, Intelligent Diagnosis of Liver Diseases from Ultrasonic Liver Images: Neural Network Approach, 13th International Conference on Biomedical Engineering, 2008, pp [10] J. Jabez Christophera, H. Khanna Nehemiaha, A. Kannan, A Swarm Optimization approach ISBN:

6 for clinical knowledge mining, Computer methods and programs in biomedicine, 121, 2015, [11] B. V. Ramana, M. S. P. Babu, N. B. Venkateswarlu, A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis, International Journal of Database Management Systems, Vol.3, 2011, pp [12] H. Sug, Improving the Prediction Accuracy of Liver Disorder Disease with Oversampling, Applied Mathematics in Electrical and Computer Engineering, , 2012 [13] D.J. Newman, S. Hettich, C.L. Blake, & C.J. Merz, UCI Repository of machine learning databases, (1998), y.html, Irvine, CA: University of California, Department of Information and Computer Science, last access: 11th August [14] Ebenezer O. Olaniyi, Khashman Adnan, Onset diabetes diagnosis using Artificial neural network, International Journal of Scientific and Engineering Research, vol 5, 2014, pp ISBN: