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Fast and effective heart attack prediction system using non linear
1.
International Journal of
Computer and Technology (IJCET), ISSN 0976 – 6367(Print), International Journal of Computer Engineering Engineering and Technology (IJCET), ISSN 0976May - June Print) © IAEME ISSN 0976 – 6375(Online) Volume 1, Number 1, – 6367( (2010), ISSN 0976 – 6375(Online) Volume 1 IJCET Number 1, May - June (2010), pp. 196-206 ©IAEME © IAEME, http://www.iaeme.com/ijcet.html FAST AND EFFECTIVE HEART ATTACK PREDICTION SYSTEM USING NON LINEAR CELLULAR AUTOMATA N.S.S.S.N Usha devi Post Graduate Student of C.S.E University College of Engineering, JNTU Kakinada E-mail: usha_nedunuri@yahoo.com L.Sumalatha Head, Department of C.S.E University College of Engineering, JNTU Kakinada E-mail: ls.cse.kkd@jntukakinada.edu.in ABSTRACT These days the Cellular Automata based Classifier have been widely used as tool for solving many decisions modeling problems. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper have proposed a Cellular Automata Classifier, Non Linear Fuzzy Multiple Attractor Cellular Automata (NNFMACA) for the prediction of Heart attack. A set of experiments was performed on a sample database of 5000 patients’ records, 13 input variables (Age, Blood Pressure, Angiography’s report etc.) are used for training and testing of the Cellular Automata Classifier. The performances of the NNFMACA were evaluated in terms of training performances and classification accuracies and the results showed that the proposed NNFMACA model has great potential in predicting the heart disease. Keywords: Cellular Automata, Data Sets, Heart Attack, NNFMACA I. INTRODUCTION Clinical decisions are often made based on doctor’s intuition and experience rather than on the knowledge rich data hidden in the database. This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service 196
2.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME provided to patients. A major challenge facing healthcare organizations (hospitals, medical centres) is the provision of quality services at affordable costs. Quality service implies diagnosing patients correctly and administering treatments that are effective. Poor clinical decisions can lead to disastrous consequences which are therefore unacceptable. Hospitals must also minimize the cost of clinical tests. A majority of areas related to medical services such as prediction of effectiveness of surgical procedures, medical tests, medication, and the discovery of relationships among clinical and diagnosis data also make use of Data Mining methodologies . Proffering valuable services at reasonable costs is a chief confront envisaged by the healthcare organizations (hospitals, medical centres). Valuable quality service refers to the precise diagnosis of patients and proffering effective treatment. Poor clinical decisions may result in catastrophes and so are not entertained. It is also necessary that the hospitals reduce the cost of clinical test. This can be attained by the making use of proper computer-based information and/or decision support systems. Prevention of HD can be approached in many ways including health promotion campaigns, specific protection strategies, life style modification programs, early detection and good control of risk factors and constant vigilance of emerging risk factors. II. DESCRIPTION OF DATABASE The heart-disease data base at Meenakshi Medical College, Kanchipuram consists of 500 cases where the disorder is one of four types of heart-disease or its absence. III. RELATED WORKS A novel technique to develop the multi-parametric feature with linear and nonlinear characteristics of HRV (Heart Rate Variability) was proposed by Heon Gyu Lee et al.. Statistical and classification techniques were utilized to develop the multi- parametric feature of HRV. Besides, they have assessed the linear and the non-linear properties of HRV for three recumbent positions, to be precise the supine, left lateral and right lateral position. Numerous experiments were conducted by them on linear and nonlinear characteristics of HRV indices to assess several classifiers, e.g., Bayesian 197
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME classifiers, CMAR (Classification based on Multiple Association Rules), C4.5 (Decision Tree) and SVM (Support Vector Machine). SVM surmounted the other classifiers. A model Intelligent Heart Disease Prediction System (IHDPS) built with the aid of data mining techniques like Decision Trees, Naïve Bayes and Neural Network was proposed by Sellappan Palaniappan et al.. The results illustrated the peculiar strength of each of the methodologies in comprehending the objectives of the specified mining objectives. IHDPS was capable of answering queries that the conventional decision support systems were not able to. It facilitated the establishment of vital knowledge, e.g. patterns, relationships amid medical factors connected with heart disease. IHDPS subsists well being web-based, user-friendly, scalable, reliable and expandable. IV. CELLULAR AUTOMATA 4.1CELLULAR AUTOMATA (CA) AND FUZZY CELLULAR AUTOMATA (FCA) A CA[6],[8] , consists of a number of cells organized in the form of a lattice. It evolves in discrete space and time. The next state of a cell depends on its own state and the states of its neighbouring cells. In a 3-neighborhood dependency, the next state qi (t + 1) of a cell is assumed to be dependent only on itself and on its two neighbours (left and right), and is denoted as qi(t + 1) = f (qi−1(t), qi(t), qi+1(t)) th th Where qi (t) represents the state of the i cell at t instant of time, f is the next state function and referred to as the rule of the automata. The decimal equivalent of the next state function, as introduced by Wolfram, is the rule number of the CA cell. In a 2- state 3-neighborhood CA, there are total 256 distinct next state functions. 4.2 FCA FUNDAMENTALS FCA [2], [6] is a linear array of cells which evolves in time. Each cell of the array assumes a state qi, a rational value in the interval [0, 1] (fuzzy states) and changes its state according to a local evolution function on its own state and the states of its two neighbours. The degree to which a cell is in fuzzy states 1 and 0 can be calculated with 198
4.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME the membership functions. This gives more accuracy in finding the coding regions. In a FCA, the conventional Boolean functions are AND , OR, NOT. V NFMACA BASED PATTERN CLASSIFIER NFMACA [13] classifies a given set of patterns into k distinct classes, each class containing the set of states in the attractor basin. A NFMACA is a special class of FCA that can efficiently model an associative memory to perform pattern recognition classification task. Its state transition behaviour consists of multiple components - each component, as noted in Figure 1, is an inverted tree, each rooted on a cyclic state. A cycle in a component is referred to as an attractor. In the rest of the paper we consider only the NFMACA having the node with self loop as an attractor state. The states in the tree rooted on an attractor form an attractor basin. Figure1 Inverted tree EXAMPLE 1: Let us have two pattern sets S1 ={(0.00,0.00, 0.25), (0.00, 0.25, 0.00), (0.25, 0.25, 0.00), (0.00,0.50, 0.00), (0.00, 0.00, 0.00), (0.25, 0.00, 0.00), (0.50,0.00, 0.00), (0.00, 0.00, 0.25), (0.00, 0.00, 0.75), (0.00,0.50,0.25)} (Class I) S2 = {(0.75, 1.00, 0.00), (1.00,0.75, 0.50), (1.00, 1.00, 1.00), (0.75, 1.00, 1.00),(1.00,1.00, 0.75), (1.00, 0.75, 1.00), (0.50, 0.75, 1.00), (1.00,0.75, 0.75), (0.75, 1.00, 0.75), (0.75, 0.75, 1.00)} (Class II) with three attributes. In order to classify these two pattern sets into two distinct classes, Class I and II respectively, we have to design a NFMACA such that the patterns of each class falls in distinct attractor basins. The basins have certain properties depending on the input loaded, it will go to autonomous state and it gives the result. When the NFMACA is loaded with an input 199
5.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME pattern say P = (1.00, 0.50, 0.00) and is allowed to run in autonomous mode, it travels through a number of transient states and ultimately reaches an attractor state (0.50, 0.50, 0.00) the attractor representing Class II. Here (0.00, 0.25, 0.00), (0.50, 0.50, 0.00) are attractor basins named b, d respectively. 5.1 NFMACA BASED TREE-STRUCTURED CLASSIFIER Like decision tree classifiers, NFMACA based tree structured classifier recursively partitions the training set to get nodes (attractors of a NFMACA) belonging to a single class. Each node (attractor basin) of the tree is either a leaf indicating a class; or a decision (intermediate) node which specifies a test on a single NFMACA. Suppose, we want to design a NFMACA based pattern classifier to classify a training set S = {S1, S2, · , SK} into K classes. First, a NFMACA with k-attractor basins is generated. The training set S is then distributed into k attractor basins (nodes). Let, S’ be the set of elements in an attractor basin. If S’ belongs to only one class, then label that attractor basin for that class. Otherwise, this process is repeated recursively for each attractor basin (node) until all the examples in each attractor basin belong to one class. Tree construction is reported in. The above discussions have been formalized in the following algorithm. We are using genetic algorithm classify the training set. ALGORITHM 1: NFMACA TREE BUILDING Input: Training set S = {S1, S2, · ·, SK} db sets Output: NFMACA Tree with disjoint disease parameters. Partition(S, K) Step 1: Generate a NFMACA with k number of attractor basins with db. Step 2: Distribute S into k attractor basins (nodes) with disease parameters. Step 3: Evaluate the distribution of examples in each attractor basin (node). Step 4: If all the examples (S’) of an attractor basin (node) belong to only one class, then label the attractor basin (leaf node) for that class Step 5: If examples (S’) of an attractor basin belong to K’ number of classes, then Partition (S’, K’). Step 6: Stop. 200
6.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME VI EXPERIMENTAL RESULTS We have developed a Non Linear Fuzzy Multiple Attractor Cellular Automata Classifier for both testing and training Figure 2-5. The interfaces and results are displayed below. 6.1 HEART ATTACK PREDICTION The design of the intelligent and effective heart attack prediction system with the aid of CA network is presented in this section. The method primarily based on the information collected from precedent experiences and from current circumstances, which visualizes something as it may occur in future, is known as prediction. The degree of success differs every day, in the process of problem solving on basis of prediction. CA networks are one among the widely recognized Artificial Intelligence (AI) machine learning models, and a great deal has already been written about them. A general conviction is that the number of parameters in the network needs to be associated with the number of data points and the expressive power of the network. EXAMPLE: If Male And age < 30 And CA Smoking = Never And Overweight = No And Alcohol = Never And Stress = No And High saturated fat diet (hsfd) = No And High salt diet (hsd) = No And Exercise = CA normal And Sedentary Lifestyle (Inactivity) = No And Hereditary = No And Bad Cholesterol = Low And NCA BLOOD Sugar = CA normal And NCA BLOOD Pressure = CA normal And Heart Rate = CA normal Or Male And age > 50 and age < 70 And Smoking = Current And Overweight = No And Alcohol = Past And Stress = No And High saturated fat diet (hsfd) = No And High salt diet (hsd) = Yes And Exercise = High And Sedentary Lifestyle (Inactivity) = No And Hereditary = No And Bad Cholesterol = Low And NCA BLOOD Sugar = Normal And NCA BLOOD Pressure = Normal And Heart Rate = Normal Then Risk Level = Normal 201
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME Figure 2 Training Interface 202
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME Figure 3 Target Vs Best Fit 203
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME Figure 4 Testing Interface Figure 5 Testing Interface 204
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME VII. CONCLUSIONS In this paper, we have presented Fast and effective heart attack prediction methods using Non Linear Fuzzy Multiple Attractor Cellular Automata.. Firstly, we have provided an efficient approach for the extraction of significant patterns from the heart disease for the efficient prediction of heart attack ,Based on the calculated significant weight age at the NFMACA tree, the patterns having value greater than a predefined threshold were chosen for the valuable prediction of heart attack. Five goals are defined based on business intelligence and data exploration. The goals are to be evaluated against the trained models. . We also tested the proposed classifier with 30,000 real time data sets and it was found very effective in predicting the heart attack. VIII. ACKNOWLEDGMENT I thank all the faculty members of department of C.S.E, University College of Engineering, JNTU Kakinada for their valuable support during my project. I also thank Dr J.V.R Murthy and Dr M.H.M Krishna Prasad for their valuable suggestions during my project period. I also thank Dr K.Karnan, Chief Superintendent of Meenakshi Medical College, Kanchipuram for providing the real time data sets. Finally I thank all my fellow class mates for their consistent encouragement. IX. REFERENCES [1] Sellappan Palaniappan, Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.8, August 2008. [2] Franck Le Duff, Cristian Munteanb, Marc Cuggiaa, Philippe Mabob, "Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method", Studies in health technology and informatics, Vol. 107, No. Pt 2, pp. 1256-9, 2004. [3] Shantakumar B.Patil al. “Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network”, European Journal of Scientific Research, ISSN 1450-216X Vol.31 No.4 (2009), pp.642-656 205
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International Journal of
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