NeuroSVM: A Graphical User Interface for Identification of Liver Patients

Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  Carla E. Brodley,et al.  Identifying and Eliminating Mislabeled Training Instances , 1996, AAAI/IAAI, Vol. 1.

[4]  R. R. Deshmukh,et al.  Data Mining Classification , 2016 .

[5]  J. Anuradha,et al.  Classification and Rule Extraction using Rough Set for Diagnosis of Liver Disease and its Types , 2011 .

[6]  N. B. Venkateswarlu,et al.  A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis , 2011 .

[7]  Hoon Jin,et al.  Decision Factors on Effective Liver Patient Data Prediction , 2014, BSBT 2014.

[8]  Bart Baesens,et al.  Filter‐ versus wrapper‐based feature selection for credit scoring , 2005, Int. J. Intell. Syst..

[9]  Shapla Rani Ghosh,et al.  A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis , 2016 .

[10]  Kim Larsen,et al.  Generalized Naive Bayes Classifiers , 2005, SKDD.

[11]  Chao Sima,et al.  Performance of Feature Selection Methods , 2009, Current genomics.

[12]  Witold R. Rudnicki,et al.  Feature Selection with the Boruta Package , 2010 .

[13]  G.Sophia Reena,et al.  Analysis of Liver Disorder Using Data mining Algorithm , 2010 .

[14]  R. Sathya,et al.  Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification , 2013 .

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Min-Jung Song,et al.  An Electrochemical Biosensor Array for Rapid Detection of Alanine Aminotransferase and Aspartate Aminotransferase , 2009, Bioscience, biotechnology, and biochemistry.

[17]  Rajan Vohra,et al.  Liver Patient Classification Using Intelligent Techniques , 2014 .

[18]  Mai S. Mabrouk,et al.  A Study of Support Vector Machine Algorithm for Liver Disease Diagnosis , 2014 .

[19]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .