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Successive iteration method for reconstruction of missing data
1.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 437 SUCCESSIVE ITERATION METHOD FOR RECONSTRUCTION OF MISSING DATA T.Jayalakshmi CMS College of Science and Commerce, Bharathiar University, India ABSTRACT The information age has made a large amount of data available for medical information processing. Occasional failures lead to missing data. The missing data may make it difficult to apply analytical models. Data imputation techniques help us fill the missing data with a reasonable prediction of what the missing values would have been. The implemented system creates successive iteration method and various other missing value techniques. The system achieves better performance than other methods. Keywords: Artificial Neural Networks; Back Propagation Method; Diabetes Mellitus; Missing Value Analysis, Pre Processing Methods; Successive Iteration Method. INTRODUCTION ANN is a type of massively parallel computing architecture based on a brain like behaviour. They work in a similar way as human brain is functioning. The neuron or nerve cell is the basic building block of the brain. Neurons are connected in various ways to form biological neural networks. They process information in the brain, and communicate information from different parts of the body to the brain. In total, the human brain has 1014 to 1015 neurons [11]. Neurons receives inputs process them by a simple connections and threshold operations and outputs a result. Neural networks are sophisticated modeling techniques capable of modeling extremely complex functions. They are inspired by biological models of neurological systems and is an established machine learning model with robust learning properties and simple deployment. Nowadays, they are being successfully applied across a wide range of problem domains, in areas like finance, medicine, engineering, pattern recognition and image processing. Anywhere, that there are problem of prediction or classification neural networks are being introduced. The principle advantage of neural network is to generalize, adapting to signal distortion and noise without loss of robustness. The back propagation algorithm is a very INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), pp. 437-447 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
2.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 438 popular training algorithm in neural network research. Previous studies show that the architecture of network can have significant effect on its performance. The performance of the network also depends on the data given to the system. Real time processing applications that are highly dependent on data often suffer from the problem of missing input variables. Data incompleteness and estimation are important problems that have received relatively little attention in the medical informatics research community. Most of the real world data are seldom complete [20]. The problem of missing data poses difficulty in the analysis and decision-making process [8]. Decision-making is highly depending on these data, requiring methods of estimation that are accurate and efficient. Neural networks cannot interpret missing values; it requires complete set of data for improving the performance [4]. There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing variable value with an estimated value or imputation obtained from the non-missing values of other variables in the same unit. Some intelligent techniques are needed to replace the missing data. Diabetes is a disease that is characterized by an elevated blood glucose level. This can be caused by a reduction of the production of insulin by the pancreas (Type I diabetes) or by the insulin being less effective at moving glucose out of the blood stream and into cells that need it (Type II diabetes). The blood glucose level that is elevated for a long period can result in metabolic complications such as kidney failure, blindness, and an increased chance of heart attacks. To prevent or postpone such complications strict control over the diabetic blood glucose level is needed [7]. The aim of this study is to classify the Type II diabetes of Pima Indian Diabetes data set. It is the most challenging problem in machine learning because of the high noise level [15][16].To remove the noise and achieve the efficient classification two important techniques were introduced: missing value analysis techniques and preprocessing techniques. This paper presents an alternative to the usual iterative method for determining the approximate value. The method developed is based on the standard numerical analysis technique of successive approximations or iterations. Reconstruction of missing values may shift input patterns, and the network may have to settle on a complicated solution to satisfy all reconstructed patterns. Successive approximations give correct results comparing to other methods. This paper organized as follows: Section II briefs about the background study, Section III describes about Artificial neural networks, Section IV deals about diabetes mellitus, Section V gives the methodology of the proposed system, and Section VI concludes the paper. BACKGROUND STUDY Siti Farhanah Bt Jaafar and Darmawati Mohd Ali [26] proposes a network with eight inputs and four inputs and the results obtained are compared in terms of error. The highest performance is obtained when the network consists of eight inputs with three hidden layers with 15, 14, 14, 1 neurons respectively. Amit Gupta and Monica Lam [2] investigate the generalization power of a modified back propagation training algorithm such as weight decay. The effect of the weight decay method with missing values can be applied for different data sets such as EPS data set, ECHO data set, IRIS data set etc. The missing values can be reconstructed using standard back propagation, iterative multiple regression, replace by average and replace by zero. The weight decay method achieves significant improvement
3.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 439 over the standard back propagation method. Fulufhelo V.Nelwamondo, Shakir Mohamed and Tshilidzi Marwala [8] discuss the expectation maximization algorithm and the auto associative neural network and genetic algorithm combination. The results show that for some variables EM algorithm is able to produce better accuracy while for the other variables the neural network and GA system is better. The findings shows that the methods used are highly problem dependent. Colleen M.Ennett, Monique Frize, C.Robin Walker [4] investigates the impact of ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets. They proposes three approaches such as delete all the values, Replace with mean, and Replace with normal to predict Canadian neonatal Intensive care unit network’s database. The experimental results were very promising. Junita Mohamad-saleh and Brain [ 12] proposes the Principle Component Analysis method for elimination of correlated information in data. It has been applied to the Electrical Capacitance Tomography data, which contains highly correlated due to overlapping sensing areas. It can boost the generalization capability of a MLP, the PCA technique also reduces the network training time due to the reduction in the input space dimensionality. The findings suggest that PCA data processing method useful for improving the performance of MLP systems, particularly in solving complex problems. Pasi Luukka [21] presented a new approach using a similarity based Yu’s norms for the detection of erythemato squamous diseases, diabetes, breast cancer, lung cancer and lymphography. The domain contains records of patients with known diagnosis. The results are very promising when using Yu’s norms for the diagnosis of patients taking into consideration the error rate. The use of this preprocessing method enhanced the result over 30%. Stavros J.Perantonis and Vassilis Virvilis [27] proposed a method for constructing salient features from a set of features that are given as input to a feed forward neural network used for supervised learning. The method exhibits some similarity to principle component analysis, but also takes into account supervised character of the learning task. It provides a significant increase in generalization ability with considerable reduction in the number of required input features. ARTIFICIAL NEURAL NETWORKS A Neural Network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experiential knowledge and making it available for use. It is very sophisticated modeling technique capable of modeling extremely complex functions. ANNs attempt to create machines that work in a similar way to the human brain by building them using components that behave like biological neurons. However the operation of artificial neural networks and artificial neuron is far more simplified that the operation of the human brain. The brain consists of millions of these neurons, which may be specialized in some task or not. The behaviour of the brain inspired to devise an artificial neuron called perceptron, which is the basis of all neural network models. It resembles the brain in two respects a) Knowledge is acquired by the networks from the environment through a learning process. b) Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge. Neural networks have advantages over classical statistical approaches especially when the training set size is small compared with the dimensionality of the problem to be solved and the underlying data distribution is unknown. Learning is essential to most of the neural network models. Learning can be supervised, when the network is provided with the correct answer for the output during training, or unsupervised, when no external teacher is present. E is the error calculated from
4.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 440 the actual output (d) to the calculated output (o). The goal of the neural network learning is to iteratively adjust weights, in order to globally minimize a measure of the difference between the actual output of the network and the desired output as specified by the teacher. DIABETES MELLITUS Diabetes mellitus is the most common endocrine disease. The disease is characterized by metabolic abnormalities and by long-term complications involving the eyes, kidneys, nerves, and blood vessels. The diagnosis of symptomatic diabetes is not difficult. When a patient presents with signs and symptoms attributable to an osmotic diuresis and is found to have hyperglycemia essentially all physicians agree that diabetes is present. The two major types of diabetes are Type I diabetes and Type II diabetes. Type I diabetes usually diagnosed in children and young adults, and was previously known as juvenile diabetes [22] [26]. Type I diabetes mellitus (IDDM) patients do not produce insulin due to the destruction of the beta cells of the pancreas. Therefore, therapy consists of the exogenous administration of insulin. Type II diabetes is the most common form of diabetes. Type II diabetes mellitus (NIDDM) patients do produce insulin endogenously but the effect and secretion of this insulin are reduced compared to healthy subjects [6]. Currently cure does not exist for the diabetes, then only option is to take care of the health of people affected, maintained their glucose levels in the blood to the nearest possible normal values [9]. METHODOLOGY The information age has made a large amount of data available for medical information processing. Occasional failures lead to missing data. The missing data may make it difficult to apply analytical models. Data imputation techniques help us fill the missing data with a reasonable prediction of what the missing values would have been. The implemented system compares various missing value techniques. 5.1 CLASSIFICATION DATA The problem that has been chosen for this research is to classify the Type II diabetic data using Levenberg Marquardt back propagation algorithm. To investigate the performance of the proposed neural-based method, the classifier was applied to PIMA INDIAN DIABETES dataset and has been collected from UCI machine learning repository [3]. The data set is a two-class problem either positive or negative for diabetes disease. The data set is different to classify because of the high noise level. It contains 768 data samples. Each sample consists of personal data and the results of medical examination. The individual attributes are • Number of times pregnant • Plasma glucose concentration • Diastolic blood pressure(mmHg) • Triceps skin fold thickness(mm) • 2-hour serum insulin(mu U/ms) • Body mass index(weight in kg/(height in m)) 2 • Diabetes pedigrees function • Age(years)
5.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 441 The above dataset 268 patients are having diabetes, which can be interpreted as “1” and the remaining patients are not having diabetes and can be interpreted as “0”. The network topology used for this study is 8-8-8-1. i.e. one input layer, two hidden layers and one output layer with eight input nodes, eight hidden nodes and one output node. 5.2 NETWORK ARCHITECTURE Back propagation algorithm is the most commonly used algorithm and it is the simple feed forward artificial neural networks. The algorithm adjusts network weights by error propagation from the output to the input. During the training the network minimizes the error by estimating the weights. The minimization procedure can be performed using the gradient- descent approaches, in which the set of weight vectors consisting of weights is adjusted by the learning parameters. The network parameters such as learning rate, momentum constant, training error and number of epochs can be considered as 0.9, 0.9, 1e-008 and 100 respectively. Before training the weights are initialized to random values. The reason to initialize weights with small values is to prevent saturation. To evaluate the performance of the network the entire sample was randomly divided into training and test sample. The model is tested using the standard rule of 80/20, where 80% of the samples are used for training and 20% is used for testing. In this classification method, training process is considered to be successful when the MSE reaches the value 1e-008. On the other hand the training process fails to converge when it reaches the maximum training time before reaching the desired MSE. The training time of an algorithm is defined as the number of epochs required to meet the stopping criterion. 5.3 MISSING DATA ANALYSIS The problem of missing data poses difficulty in the analysis and decision making processes. Decision making is highly depending on these data, requiring methods of estimation that are accurate and efficient. Back propagation neural networks have been applied for classification problems in real world situations. A drawback of this type of neural network is that it requires a complete set of input data, and real world data is seldom complete. The problem of databases containing missing values is a common one in the medical environment. ANNs cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty in identifying the factors leading to a rare outcome. Imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analyzed using standard techniques for complete data. The analysis should ideally take into account that there is a greater degree of uncertainty than if the imputed values had actually been observed, however, and this generally requires some modification of the standard complete-data analysis methods. 5.4 METHODS Missing information in data sets is more than a common scenario. There are different methods to perform these imputations depending on the type of variable with missing data and on the type of auxiliary variables. Here the imputation of categorical variables from other numerical and categorical variables is studied. This paper presents an alternative to the usual method for determining the approximate value. It is an alternative to the usual iterative method for determining the approximate value. Iterative algorithms usually used when explicit formulae are unavailable. The idea is that a repetition of simple calculations will
6.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 442 result in a sequence of approximate values for the quantity of interest. The method developed is based on the standard numerical analysis technique of successive approximations or iterations. Successive approximations give correct results comparing to other methods. The mean of the same attribute is repeatedly calculated until it approximates the value. The approximated value is replaced with the missing column. a) Omit the Values: The easiest way to deal with missing values is simply delete all the cases with missing values for the variable under consideration. This technique however may lead to the loss of potentially valuable information about patients whose values are missing. b) Replace with Mean: The second approach is to replace all missing values with the mean. The method of replacing by average is to replace all missing values of an attribute by the average of all available values of the same attribute in the training set. Replacing the missing values with the means might bias the databases towards the sicker ones. c) Replace with Zero: The third technique is to replace all the missing values with zeros. The method of replacing by zero is simply to replace all missing values by zero. Replacing missing values by zero in this study has the same effect of replacing missing values by the smallest value of an attribute since data have been converted to be between zero and one. If the values are important for clinical management the assessment of missing values leads to poor classification. d) Replace with K-nearest neighbor: The fourth technique K-nearest neighbor method replaces missing values in data with the corresponding value from the nearest-neighbor column. The nearest-neighbor column is the closest column in Euclidean distance. If the corresponding value from the nearest-neighbor column is also contains missing value the next nearest column is used. e) Replace with Successive Iteration Method: The technique presented in this paper is to replace the missing data with successive iteration method. 5.4.1 ALGORITHM Step 1: Find the missing element in the data set. Step 2: Calculate the mean of the attribute Step 3: Substitute the mean with missing attributes Step4: Find the new mean of the attribute Step 5: Compare the new mean with the existing mean Step 6: If both are same replace the mean with missing column attribute Step 7: Otherwise repeat the steps 3-6 until the mean converges. 5.5 PREPROCESSING OF INPUT DATA Neural network training could be made more efficient by performing certain preprocessing steps on the network inputs and targets. Network input processing functions transforms inputs into better form for the network use. The normalization process for the raw inputs has great effect on preparing the data to be suitable for the training. Without this normalization, training the neural networks would have been very slow. There are many types of data normalization. It can be used to scale the data in the same range of values for each input feature in order to minimize bias within the neural network for one feature to another. Data normalization can also speed up training time by starting the training process for each feature within the same scale. It is especially useful for modeling application where
7.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 443 the inputs are generally on widely different scales. Principle Component Analysis is a very popular normalization method. Principal Component’s normalization is based on the premise that the salient information in a given set of features lies in those features that have the largest variance. This means that for a given set of data, the features that exhibit the most variance are the most descriptive for determining differences between sets of data. This is accomplished by using eigenvector analysis on either the covariance matrix or correlation matrix for a set of data. 5.6 EXPERIMENTAL RESULTS The system has been implemented to study the impact of pre-processing and missing value techniques. The simulations have been carried out using MATLAB. Various networks were developed and tested with random initial weights. The network is trained ten times and the performance goal is achieved at different epochs. Figure. 1. SIT Method without pre-processing Figure. 2. SIT Method with pre-processing Figure.3. Accuracy (without pre-processing)
8.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 444 Figure.4. Accuracy (With pre-processing) TABLE I. IMPACT OF MISSING VALUES WITHOUT PRE-PROCESSING Accuracy 62 64 66 68 70 72 74 76 Omit the Values Replace with Zero Replace with Mean Replace with KNN Replace with SIT Method Accuracy Performance 0 20 40 60 80 100 120 Omit the Values Replace with Zero Replace with Mean Replace with KNN Replace with SIT Method Epochs Accuracy Epochs Performance 1e-009 Method Accuracy Epochs Performance 1e-009 Omit the Values 97.1 24 5.76 Replace with Zero 99 15 5.11 Replace with Mean 99.2 14 6.48 Replace with KNN 99 35 8.33 Replace with SIT 99.5 14 5.88
9.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 445 TABLE II. IMPACT OF MISSING VALUES WITH PRE-PROCESSING The impact of the missing values can be assessed by taking the average of ten runs and measured in terms of classification accuracy (Table.1) and training time (Table.2). It shows that the accuracy was improved when replace with successive iteration method in combination of PCA pre-processing method. 6 CONCLUSION This paper demonstrates the impact of missing value technique and pre-processing technique. It proves that some combination of missing values and pre-processing the accuracy was tremendously improved. This shows that pre-processing and missing values play a major role in classification. In future this can be applied for different training methods and networks REFERENCES [1] Ambrosiadou, V., Gogou, G., Pappas, C., and Maglaveras, N., “Decision support for insulin regime prescription based on a neural network approach”, Medical Informatics, 1996, pp.23–34. [2] Amit Gupts and Monica Lam (1998), The Weight Decay backpropagation for generalizations with missing values, Annals of Operations Research, Science publishers, pp.165-187. [3] G. Arulampalam and A Bouzerdoum, “Application of shunting Inhibitory Artificial Neural Networks to Medical Diagnosis”, Seventh Australian and New Zealand Intelligent Information Systems Conference,18-21 November 2001, pp.89 – 94. [4] Colleen M Ennett, Monique Frize, C.Robin Walker, “Influence of Missing Values on Aritificial Neural Network Performance”, Proceedings of Medinfo, 2001, pp.449-453. [5] DeClaris, N., and Su, M. C., “A neural network based approach to knowledge acquisition and expert systems”, IEEE Systems Man and Cybernetics Proc. Pp.645– 650, 1991. [6] Edgar Teufel1, Marco Kletting1, Werner G.Teich2, Hans-Jorg Pfleiderer1, and Cristina Tarin-Sauer3, “Modelling the Glucose Metabolism with Backpropagation Through Time Trained Elman Nets”, IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP'03, 17-19 Sept. 2003, pp.789 - 798 [7] Eng Khaled Eskaf, Prof.Dr.Osama ,Badawi , Prof.Dr.Tim Ritchings, “Predicting blood Glucose Levels in Diabetics using feature Extraction and Artificial Neural Networks”. Method Accuracy Omit the Values 72.41 Replace with Zero 67.84 Replace with Mean 66.53 Replace with KNN 70.99 Replace with SIT 75.29
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 446 [8] Fuluf helo V Nelwamondo, Shakir Mohammed and Tshilidzi Mawala, “Missing Data: A comparison of neural network and expectation maximization techniques”, Current Science, Vol 93, No 11, 2007. [9] Humberto M.Fonseca; Victor H.Ortiz, Agustin LCabrera., “Stochastic Neural Networks Applied to Dynamic Glucose Model for Diabetic Patients”, 1st ICEEE, 2004, pp.522 - 525 [10] Igor Aizenberg, Claudio Moraga, “Multilayer Feedforward Neural Network Based on Multi-Valued neurons (MLMVN) and a back propagation learning algorithm”, Soft Computing, 20 April 2006, pp.169-183. [11] Jain.A.K, J.Mao and K.M.Mohuddin (1996), Artificial Neural Networks: A Tutorial, IEEE Computer, Vol.29,No.3,PP.31-44. [12] Junita Mohammad-saleh, Brain S.Hoyle, “Improved Neural Network performance using Principle Component Analysis on Matlab”, Int. journal of the computer, the internet and management, Vol.16, No 2, 2008, pp.1-8. [13] Lakatos,G., Carson, E. R., and Benyo, Z., “Artificial neural network approach to diabetic management”, Proceedings of the Annual International Conference of the IEEE, EMBS, pp.1010–1011, 1992. [14] Maglaveras, N., Stamkopoulos, T., Pappas, C., and Strintzis M., “An adaptive back- propagation neural network for real-time ischemia episodes detection. Development and performance analysis using the European ST-T database”, IEEE Trans. Biomed. Engng. pp.805–813, 1998. [15] Md.Monirul Islam, Md.Shahjahan, and K.Murase, “Exploring Constructive Algorithms with Stopping Criteria to Produce Accurate and Diverse Individual Neural Networks in an Ensemble”, IEEE International Conference on Systems, Man, and Cybernetics, Volume 3, 7-10 Oct. 2001 pp.1526 – 1531. [16] Md.Shahjahan1,M.A.H.Akhand2, and K.Murase1, “A Pruning Algorithm for Training Neural Network Ensembles”, SICE 2003 Annual Conference ,Volume 1, 2003, pp.628 – 633. [17] Miller,A. S., Blott,B. H., and Hames, T. K., “Review of neural network applications in medical imaging and signal processing” Med. Biol. Eng. Comput. 1992, pp.449– 464. [18] M.Nawi1, R.S. Ransing and M.R. Ransing, “An Improved Conjugate Gradient Based learning Algorithm for Back Propagation Neural Networks” International journal of Computational Intelligence, 2008. [19] Ping Zuo,Yingchun Li, Jie Ma SiLiang Ma, “Analysis of Noninvasive Measurement of Human Blood Glucose with ANN-NIR Spectroscopy”, International Conference on Neural Networks and Brain, ICNN&B '05. Volume 3, 13-15 Oct. 2005, pp. 1350 – 1353. [20] P.K.Sharpe and R.J.Solly, “Dealing with Missing Values in Neural Network based Diagnostic Systems”, Neural Computing and Applications, Springer-Verlag London Ltd, 1995, pp.73-77. [21] Pasi Luuka (2007), Similarity classifier using similarity measure derived form Yu’s norms in classification of medical data sets, Elsevier, Computer in Biology and Medicine, pp.1133-1140. [22] Rajeeb Dey and Vaibhav Bajpai, Gagan Gandhi and Barnali Dey, “Application of Artificial Neural Network (ANN) technique for Diagnosing Diabetes Mellitus”, IEEE Region 10 Colloquium and the 3rd ICIIS, Dec 2008, PID 155.
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Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 447 [23] Richard B.North, J.Paul Mcnamee, Lee wu, and Steven Paintadosi, “Artificial neural networks: Application to electrical stimulation of the human nervous system”, IEEE 1996. [24] Roelof K Brouwer PEng, PhD, “An Integer Recurrent Artificial Neural Network for Classifying Feature Vectors”. ESSANN, Proceedings – Eur. Symp on Artificial Neural Networks, April 1999, D-Facto public, pp. 307-312. [25] S.G.Mougiakakou,K.Prountzou,K.S.Nikita , “A Real Time Simulation Model of Glucose-Insulin Metabolism for Type 1 Diabetes Patients” , 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, IEEE-EMBS 17-18 Jan. 2006 pp.298 - 301. [26] Siti Farhanah, Bt Jafan and Darmawaty Mohd Ali, “Diabetes Mellitus Forecast using Artificial Neural Networks (ANN)”, Asian Conference on sensors and the international conference on new techniques in pharamaceutical and medical research proceedings (IEEE), Sep 2005, pp. 135-138. [27] Stavros J.Perantonis and Vassilis Virvilis (1999), Input Feature Extraction for Multilayered Perceptrons Using Supervised Principle Component Analysis, Neural processing letters, pp.243-252. [28] T.Waschulzik, W.Brauer, T.Castedello, B.Henery, “Quality Assured Efficient Engineering of Feedforward Neural Networks with Supervised Learning (QUEEN) Evaluated with the pima Indian Diabetes Database”, Proceedings of the IEEE-INNS- ENNS International Joint Conference on Neural Networks, IJCNN, Volume 4, 24-27 July 2000 pp.97 – 102. [29] Chaitrali S. Dangare and Dr. Sulabha S. Apte, “A Data Mining Approach for Prediction of Heart Disease using Neural Networks” International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 30-40, ISSN Print : 0976 – 6367, ISSN Online : 0976 – 6375. [30] D. Kanakaraja, P. Hema And K. Ravindranath, “Comparative Study on Different Pin Geometries of Tool Profile in Friction Stir Welding using Artificial Neural Networks”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2012, pp. 245-253, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [31] Y. Angeline Christobel and P. Sivaprakasam, “Improving the Performance of K- Nearest Neighbor Algorithm for the Classification of Diabetes Dataset with Missing Values”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 155-167, ISSN Print : 0976 – 6367, ISSN Online : 0976 – 6375.
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