Fast and effective heart attack prediction system using non linear

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  • 1. International Journal of Computer and Technology (IJCET), ISSN 0976 – 6367(Print),International Journal of Computer Engineering Engineeringand Technology (IJCET), ISSN 0976May - June Print) © IAEMEISSN 0976 – 6375(Online) Volume 1, Number 1, – 6367( (2010),ISSN 0976 – 6375(Online) Volume 1 IJCETNumber 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.inABSTRACT These days the Cellular Automata based Classifier have been widely used as toolfor solving many decisions modeling problems. Medical diagnosis is an important butcomplicated task that should be performed accurately and efficiently and its automationwould be very useful. A system for automated medical diagnosis would enhance medicalcare and reduce costs. In this paper have proposed a Cellular Automata Classifier, NonLinear Fuzzy Multiple Attractor Cellular Automata (NNFMACA) for the prediction ofHeart 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 fortraining and testing of the Cellular Automata Classifier. The performances of theNNFMACA were evaluated in terms of training performances and classificationaccuracies and the results showed that the proposed NNFMACA model has greatpotential 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 experiencerather than on the knowledge rich data hidden in the database. This practice leads tounwanted 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), © IAEMEprovided to patients. A major challenge facing healthcare organizations (hospitals,medical centres) is the provision of quality services at affordable costs. Quality serviceimplies diagnosing patients correctly and administering treatments that are effective. Poorclinical decisions can lead to disastrous consequences which are therefore unacceptable.Hospitals must also minimize the cost of clinical tests. A majority of areas related tomedical services such as prediction of effectiveness of surgical procedures, medical tests,medication, and the discovery of relationships among clinical and diagnosis data alsomake use of Data Mining methodologies . Proffering valuable services at reasonablecosts is a chief confront envisaged by the healthcare organizations (hospitals, medicalcentres). Valuable quality service refers to the precise diagnosis of patients and profferingeffective treatment. Poor clinical decisions may result in catastrophes and so are notentertained. It is also necessary that the hospitals reduce the cost of clinical test. This canbe attained by the making use of proper computer-based information and/or decisionsupport systems. Prevention of HD can be approached in many ways including healthpromotion campaigns, specific protection strategies, life style modification programs,early detection and good control of risk factors and constant vigilance of emerging riskfactors.II. DESCRIPTION OF DATABASE The heart-disease data base at Meenakshi Medical College, Kanchipuram consistsof 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 andnonlinear characteristics of HRV (Heart Rate Variability) was proposed by Heon GyuLee 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-linearproperties of HRV for three recumbent positions, to be precise the supine, left lateral andright lateral position. Numerous experiments were conducted by them on linear andnonlinear characteristics of HRV indices to assess several classifiers, e.g., Bayesian 197
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEMEclassifiers, CMAR (Classification based on Multiple Association Rules), C4.5 (DecisionTree) and SVM (Support Vector Machine). SVM surmounted the other classifiers. A model Intelligent Heart Disease Prediction System (IHDPS) built with the aidof data mining techniques like Decision Trees, Naïve Bayes and Neural Network wasproposed by Sellappan Palaniappan et al.. The results illustrated the peculiar strength ofeach of the methodologies in comprehending the objectives of the specified miningobjectives. IHDPS was capable of answering queries that the conventional decisionsupport systems were not able to. It facilitated the establishment of vital knowledge, e.g.patterns, relationships amid medical factors connected with heart disease. IHDPS subsistswell being web-based, user-friendly, scalable, reliable and expandable.IV. CELLULAR AUTOMATA4.1CELLULAR AUTOMATA (CA) AND FUZZY CELLULARAUTOMATA (FCA) A CA[6],[8] , consists of a number of cells organized in the form of a lattice. Itevolves in discrete space and time. The next state of a cell depends on its own state and thestates of its neighbouring cells. In a 3-neighborhood dependency, the next state qi (t + 1) of acell is assumed to be dependent only on itself and on its two neighbours (left and right), and isdenoted 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 nextstate function and referred to as the rule of the automata. The decimal equivalent of thenext 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 arrayassumes a state qi, a rational value in the interval [0, 1] (fuzzy states) and changes itsstate according to a local evolution function on its own state and the states of its twoneighbours. 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), © IAEMEthe membership functions. This gives more accuracy in finding the coding regions. In aFCA, 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 classcontaining the set of states in the attractor basin. A NFMACA is a special class of FCAthat can efficiently model an associative memory to perform pattern recognitionclassification task. Its state transition behaviour consists of multiple components - eachcomponent, as noted in Figure 1, is an inverted tree, each rooted on a cyclic state. A cyclein a component is referred to as an attractor. In the rest of the paper we consider only theNFMACA having the node with self loop as an attractor state. The states in the treerooted on an attractor form an attractor basin. Figure1 Inverted treeEXAMPLE 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 IIrespectively, we have to design a NFMACA such that the patterns of each class falls indistinct attractor basins. The basins have certain properties depending on the input loaded, it will go toautonomous 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), © IAEMEpattern say P = (1.00, 0.50, 0.00) and is allowed to run in autonomous mode, it travelsthrough 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) areattractor basins named b, d respectively.5.1 NFMACA BASED TREE-STRUCTURED CLASSIFIER Like decision tree classifiers, NFMACA based tree structured classifierrecursively partitions the training set to get nodes (attractors of a NFMACA) belonging toa single class. Each node (attractor basin) of the tree is either a leaf indicating a class; or adecision (intermediate) node which specifies a test on a single NFMACA. Suppose, we want to design a NFMACA based pattern classifier to classify atraining set S = {S1, S2, · , SK} into K classes. First, a NFMACA with k-attractor basins isgenerated. The training set S is then distributed into k attractor basins (nodes). Let, S’ bethe set of elements in an attractor basin. If S’ belongs to only one class, then label thatattractor basin for that class. Otherwise, this process is repeated recursively for eachattractor 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 thefollowing algorithm. We are using genetic algorithm classify the training set.ALGORITHM 1: NFMACA TREE BUILDINGInput: Training set S = {S1, S2, · ·, SK} db setsOutput: 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 classStep 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), © IAEMEVI EXPERIMENTAL RESULTS We have developed a Non Linear Fuzzy Multiple Attractor Cellular AutomataClassifier for both testing and training Figure 2-5. The interfaces and results are displayedbelow.6.1 HEART ATTACK PREDICTION The design of the intelligent and effective heart attack prediction system with theaid of CA network is presented in this section. The method primarily based on theinformation collected from precedent experiences and from current circumstances, whichvisualizes something as it may occur in future, is known as prediction. The degree ofsuccess differs every day, in the process of problem solving on basis of prediction. CAnetworks are one among the widely recognized Artificial Intelligence (AI) machinelearning models, and a great deal has already been written about them. A generalconviction is that the number of parameters in the network needs to be associated withthe number of data points and the expressive power of the network.EXAMPLE:IfMale 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 AndHereditary = No And Bad Cholesterol = Low And NCA BLOOD Sugar = CA normalAnd NCA BLOOD Pressure = CA normal And Heart Rate= CA normalOr Male And age > 50 and age < 70 And Smoking = Current And Overweight = NoAnd Alcohol = Past And Stress = No And High saturated fat diet (hsfd) = No And Highsalt diet (hsd) = Yes And Exercise = High And Sedentary Lifestyle (Inactivity) = No AndHereditary = No And Bad Cholesterol = Low And NCA BLOOD Sugar = Normal AndNCA BLOOD Pressure = Normal And Heart Rate = Normal Then Risk Level = Normal 201
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEMEVII. CONCLUSIONS In this paper, we have presented Fast and effective heart attack predictionmethods using Non Linear Fuzzy Multiple Attractor Cellular Automata.. Firstly, we haveprovided an efficient approach for the extraction of significant patterns from the heartdisease for the efficient prediction of heart attack ,Based on the calculated significantweight age at the NFMACA tree, the patterns having value greater than a predefinedthreshold were chosen for the valuable prediction of heart attack. Five goals are definedbased on business intelligence and data exploration. The goals are to be evaluated againstthe trained models. . We also tested the proposed classifier with 30,000 real time data setsand 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 ofEngineering, JNTU Kakinada for their valuable support during my project. I also thankDr J.V.R Murthy and Dr M.H.M Krishna Prasad for their valuable suggestions during myproject period. I also thank Dr K.Karnan, Chief Superintendent of Meenakshi MedicalCollege, Kanchipuram for providing the real time data sets. Finally I thank all my fellowclass 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
  • 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),ISSN 0976 – 6375(Online) Volume 1, Number 1, May - June (2010), © IAEME[4] Latha Parthiban and R.Subramanian,” Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm” in International Journal of Biological and Life Sciences 3:3 2007,pp157-161.[5] K.Srinivas et al. “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 250-255.[6] Pradipta Maji, Samik Parua, Sumanta Das, and P. Pal Chaudhuri, Cellular Automata in Protein Coding Region Identification, IEEE Proceedings of 2nd International Conference on Intelligent Sensing and Information Processing (ICISIP-05), India, pp. 479--484, January 2005.[7] Pradipta Maji, Biplab K. Sikdar, and P. Pal Chaudhuri, Cellular Automata Evolution for Pattern Classification, Proceedings of 6th International Conference on Cellular Automata for Research and Industry (ACRI-04), Netherland, pp. 660--669, October 2004. 206