Identification of Liver Disease using Classification Technique Rishi Tiwari1, A. K. Shrivastav2, Akhilesh Kumar Shrivas3 1
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Dept of IT, Dr. C.V. Raman University, Bilaspur Chhattisgarh ,India Dept of Physics, Dr. C.V. Raman University, Bilaspur Chhattisgarh ,India 3 Dept of IT, Dr. C.V. Raman University, Bilaspur Chhattisgarh ,India
Abstract—In medicals science, there are large amount of information related to patient and medical conditions. To discover the relevant pattern from large volume of data could easily identify the disease condition and diagnosis of diseases condition. Data mining is one of the important techniques for analyzing and evaluation of useful data that is helpful for identifying the particular diseases. In this research work, there are various classification techniques like C4.5, CART, Random Forest (RF) , BayesNet, Support Vector Machine (SVM) , Multilayer Perceptron and its ensemble have used for analysis of liver patient data and classification of liver patient. The ensemble of C4.5, CART and RF and ensemble of CART, RF and SVM gives better results compare to other individual models. The ensemble of C4.5, CART and RF and ensemble of CART, RF and SVM gives 71.69% and 71.52% respectively. Keywords: Liver Disease, Classification, Ensemble Model. I. INTRODUCTION Liver disease is one of the critical problems for human being in medical science. Liver [1] is the largest internal organ and glandular organ in the human body, playing a major role in metabolism and serving several vital functions. The main reason for increasing liver patient is to consumption of alcohol, inhale of harmful gases, intake of contaminated food and drugs. Liver disease may not cause any symptoms at earlier stage or the symptoms may be vague, like weakness and loss of energy. Symptoms depend on the type and the extent of liver disease. Classification techniques play very important role diagnosis disease in medical science. Classifier can classify the data as liver or non-liver .There are various authors have worked in the field of classification of liver and non-liver data. H. Jin, et al.,(2014) [2] have used various classification techniques like naïve bayes, MLP, Decision tree and k-NN as classifier for classification of liver patient. They have compared proposed naïve bayes as classifier with others which given high classification accuracy compared to others. P. Sexena, et al. (2013) [3] have used various clustering algorithm for cauterization of liver patient. They have compared various cluttering algorithm and found that k-means clustering algorithm is simplest and fastest algorithm as compared to other algorithms. A. Gulia , et al. (2014) [1] have suggested classification algorithms like J48, MLP,SVM ,Random Forest and Bayes Net for classification of liver patient. Random forest technique gives highest accuracy 71.35% as best model with reduced number of features. J. Pahareeya et al. (2014) [9] have proposed Genetic programming (GP) for classification of liver patient data. The proposed model gives high classification accuracy compared to Random Forest, Multiple Linear Regression (MLR) and Support Vector Machine (SVM). II. ARCHITECTURE OF MODEL Figure 1 shows that the architecture of proposed model for classification of liver patient. In this model, Indian Liver Patient Data Set (ILPD) [4] applied with 10-fold cross validation on various individual models as well as ensemble models. An Ensemble models play very important role to increase the performance of model. The ensemble model can be developed to combine the two or more model. In this work, we propose the two ensemble model as ensemble of C4.5, CART and RF @IJRTER-2016, All Rights Reserved
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