INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET)

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1 INTERNTIONL JOURNL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN (Print) ISSN (Online) Volume 5, Issue 6, June (2014), pp IEME: Journal Impact Factor (2014): (Calculated by GISI) IJCET I E M E RTIFICIL NEURL NETWORK SED DT MINING PPROCH FOR HUMN HERT DISESE PREDICTION Shikha Dixit, ppu Kuttan. K.K Maulana zad National Institute of Technology, hopal, India 1. INTRODUCTION Human heart can be described as a compound body organ contains muscles together with biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of human such as brain, kidney etc. various study revealed that heart disease have emerged as the number one killer in world. bout 25 per cent of deaths in the age group of years occur because of heart disease. There are number of factors, which increase the risk of heart disease such as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart disease.cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease (stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic heart disease, inflammatory heart disease [5]. Heart disease prediction can help in reduction of deaths due to heart problems. Diagnosis is usually based on signs, symptoms and physical examination of a patient. Heart diagnosis is not always possible at every medical centre and due to lack of advance heart diagnosis equipment usually physicians goes through intuition and experience to diagnosis the patient. Consequently medical errors and indivisible results are reasons for computer based diagnosis system [7]. Health care industry today generates large amount of complex data about patients, hospitals resources, disease diagnosis, electronic patient records, medical devices etc. The large amounts of data act as a key source to be processed and analysed for knowledge extraction that act as support for cost-savings and decision making [3]. Now a days, computer method intelligent data processing are available and applied for this purpose and thus expert medical system can be created. One of the promising method is Data mining and rtificial neural network which is highly effective tool used in classification task as well as to solve many important problem, such as signal enhancement, identification and prediction of signals and factors. 136

2 2. RTIFICIL NEURL NETWORK rtificial Neural network (NN) are originally modelled as a computational model [4] to mimic the way of brain works. rain is made from small functional units called neurons. Each neuron connected to several other neurons by dendrites and exons. Dendrites receive the signal from other neurons and act as a input to the neuron. Similar way artificial neural network built from several computational units which are sometimes called neurons. These units are connected links and each links have a weight associate with it. Each unit computes the weighted some of the input values and transfer function transforms a final valve that act as a unit output valve. efore using any NN model it must be trained with representative data [8]. The NNcan be classified in two main groups according to the way they learn, I. Supervised learning: It is a simple model, in which the networks compute a response to each input and then compare it with target value. If the computed response differs from target value, the weights of the network are adapted according to a learning rule. E.g.: Single-layer perceptron, Multi-layer perceptron. II. Unsupervised learning: These networks learn by identifying special features in the problems they are exposed to. e.g.: Self-organizing feature maps. Neural network has following properties: Nonlinearity Learning ability Input-output mapping daptivity Evidential response Fault tolerance Neurological analogy In medical field, decision making is done by neural network because they provide more accurate results. 3. NEURL NETWORK FOR HERT DISESE PREDICTION In this study, decision support system is developed for predicting heart disease of a patient. The prediction is done based on historical heart disease database. The system uses medical terms such as sex, blood pressure and cholesterol like 12 input attributes are used(web). To get more appropriate results, two more attributes ie. Smoking and family history of coronary artery disease, as these are considered as most prominent attributes for heart disease. For this, the technique of Multilayer Perception Neural Network (MLPNN) with ackpropagation algorithm (P) is used. 137

3 3.1 Multilayer Perceptron Neural Network(MLPNN) In rtificial Neural Network the most important model is MLPNN because of its multilayer infrastructure. Figure 1: Multilayer Perceptron Neural Network The MLPNN consists of one input layer, one output layer and one or more hidden layers. Each layer consists of one or more nodes, represented by small circles. The lines between nodes indicate flow of information from one node to another node. The input layer receives the signal from external nodes. The output of input layer is given to hidden layer, through weighted connection links. It performs computations and transmits the result to output layer through weighted links. The output of hidden layer is forwarded to output layer. This output layer performs computations and produce final result[1]. The working of Multilayer perceptron neural network is summarised in steps as mentioned below: I. Input data is provided to input layer for processing, which produces a predicted output. II. The predicted output is subtracted from actual output and error value is calculated. III. The network then uses a ackpropagation algorithm which adjusts the weights. IV. For weights adjusting it starts from weights between output layer nodes and last hidden layer nodes and works backwards through network. V. When back propagation is finished, the forwarding process starts again. VI. The processes repeated until the error between predicted and actual ouput is minimized. 3.2 ackpropagation Network The most widely used training algorithm for multilayer and feed forward network is ackpropagation. The name given is back propagation because, it calculates the difference between actual and predicted values from output nodes backward to nodes in previous layer. This is done to improve weights during processing [2]. 138

4 The working of ackpropagation algorithm is summarized in steps as follows: I. Provide training data to network. II. Compare the actual and desired output. III. Calculate the error in each neuron. IV. Calculate what output should be for each neuron and how much lower or higher output must be adjusted for desired output. V. Then adjust the weights. Feed forward training pattern Calculate error Compute differences Propagate error backwards Update Weights Figure 2: ack-propagation Neural Network 4. RESULT The experiment is carried out on a publicly available database for heart disease in UCI Machine Learning Repository. The dataset is divided into 2 sets training (303 records) and testing set (270 records). Data Mining tool Weka is used for experiment. Parameters used for experiment are listed below. Patient ID: Patient Identification number. Diagnosis: Value 1:= < 50% (no heart disease) Value 0: > 50% (has heart disease) The other parameters are listed below: 139

5 Table 1: Description of 12 parameters used Sr. no. ttribute Description Values 1 ge ge in years Continuous 2 Sex Male or female 1=male 0=female 3 Cp Chest pain type 1=typical type 1 2= typical type angina 3= non-angina pain 4= asymptomatic 4 Thestbps Resting blood pressure Continuous value in mm hg 5 Chol Serum cholesterol Continuous value in mm/dl 6 Restecg Resting electrographic results 0= normal 1= having_st_t wave abnormal 2= left ventricular hypertrophy 7 Fbs Fasting blood sugar mg/dl mg/dl 8 Thalach Maximum heart rate achieved Continuous value 9 Exang Exercise induced angina 0= no 1= yes 10 Oldpeak ST depression induced by exercise Continuous value relative to rest 11 Slope Slope of the peak exercise ST segment 1= unsloping 2= flat 3= downsloping 12 Ca Number of major vessels colored by floursopy 0-3 value For getting more accurate results 2 more parameters are used i.e. smoking and Family history of coronary artery disease. Table 2: Description of newly added parameters Sr. no ttribute Description Values 13 Smoke Smoking 1=past 2=current 3=never 14 Famhist Family history of coronary artery disease 1=yes 0=no fter applying neural networks on training dataset the results obtained is shown as confusion matrix. The confusion matrix for two classifier is shown in Table: 140

6 (has heart disease) (no heart disease) Table 3: confusion matrix (has heart disease) TP FP (no heart disease) FN TN TP (True Positive): It denotes the number of records classified as true while they were actually true. FN (False Negative): It denotes the number of records classified as false while they were actually true. FP (False Positive): It denotes the number of records classified as true while they were actually false. TN (True Negative): It denotes the number of records classified as false while they were actually false. The following table shows results obtained with 12 and 14 parameters. Table 4: Results for Neural networks with 12 parameters Table 5: Results for Neural networks with 14 parameters The following table and graph showss comparison of accuracies obtained with 12 and 14 parameters: Classification Techniques Neural Networks Table 6: Comparison of accuracies ccuracy with 12 attributes 14 attributes 99.00% 99.80% ccuracy(%) parameters parameters accuracy Figure 3: Graph shows accuracy for 12 and 14 parameters 141

7 5. CONCLUSION In this research paper, the overall objective is to study the data mining and rtificial Neural Network techniques for the improvement of Heart disease prediction system. From the NN, a multilayer perceptron neural network along with back propagation algorithm is used to develop the system. The experimental results showed more accuracy i.e. 99.8% with 14 attributes. This can make a better diagnosis platform for domain experts and researchers to provide the patient with early detection of heart disease. REFERENCES 1. Chaitrali S. Dangre et al., Data Mining pproach for prediction of Heart Disease using Neural Networks, International Journal of Computer Engineering and Technology, vol 3: Issue 3,2012,pp K. Sudhakar, M. Manimekalai, Study of Heart Disease Prediction using Data Mining, International Journal of dvanced Research in Computer Science and Software Engineering, Vol 4: Issue 1, Milan Kumari, SunilaGodara, Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, IJCST Vol. 2, Issue 2, June Murphy,P.M., ha, D.W UCI Repository of machine learning databases [ Irvine, C: University of California, Department of Information and Computer Science. 5. Oleg Yu. tkov et al., Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters, Journal of Cardiology, Vol. 59, 2012, pp SameshGhwanmeh et al., Innovative rtificial Neural Networks ased Decision Support System for Heart Disease Diagnosis, Journal of Intelligent Learning Systems and pplications, Vol 5, 2013, pp S.bdul, V.D. hagile et al., Diagnosis and Medical Prescription of Heart Disease Using Support Vector Machine and Feedforward ack-propagation Technique, International Journal on Computer Science and Engineering, Vol. 1, No. 6, 2010, pp Shantakumar.Patil, Y.S.Kumaraswamy, Intelligent and Effective Heart ttack Prediction System using Data Mining and rtificial Neural Network, European Journal of Scientific Research, ISSN X, Vol.31 No.4 (2009), pp Chaitrali S. Dangare and Dr. Mrs. Sulabha S. pte, Data Mining pproach for Prediction of Heart Disease using Neural Networks, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp , ISSN Print: , ISSN Online: tul Pradhan, Vidushi Kapoor, Sanjay Kumar, Prateek Tandon and Priyanka Kumari, nalytical Techniques used for Disease Diagnosis Invasive and Non-Invasive Tools, International Journal of dvanced Research in Engineering & Technology (IJRET), Volume 4, Issue 1, 2013, pp. 9-27, ISSN Print: , ISSN Online: N.S.S.S.N Usha devi and L.Sumalatha, Fast and Effective Heart ttack Prediction System using Non Linear Cellular utomata, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp , ISSN Print: , ISSN Online: