An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)

Talking about organ failure and people immediately recall kidney diseases. On the contrary, there is no such alertness about liver diseases and its failure despite the fact that this disease is one of the leading causes of mortality worldwide. Therefore, an effective diagnosis and in time treatment of patients is paramount. This study accordingly aims to construct an intelligent diagnosis system which integrates principle component analysis PCA and k-nearest neighbor KNN methods to examine the liver patient dataset. The model works with the combination of feature extraction and classification performed by PCA and KNN respectively. Prediction results of the proposed system are compared using statistical parameters that include accuracy, sensitivity, specificity, positive predictive value and negative predictive value. In addition to higher accuracy rates, the model also attained remarkable sensitivity and specificity, which were a challenging task given an uneven variance among attribute values in the dataset.

[1]  S.A. Azaid,et al.  Automatic Diagnosis of Liver Diseases from Ultrasound Images , 2006, 2006 International Conference on Computer Engineering and Systems.

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

[3]  Alexandru G. Floares,et al.  Intelligent clinical decision supports for interferon treatment in chronic hepatitis C and B based on i-biopsy™ , 2009, 2009 International Joint Conference on Neural Networks.

[4]  Sim Heng Ong,et al.  A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images , 2012, Expert Syst. Appl..

[5]  I. Burhan Türksen,et al.  Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions , 2009, Expert Syst. Appl..

[6]  Maya R. Gupta,et al.  Bayesian Quadratic Discriminant Analysis , 2007, J. Mach. Learn. Res..

[7]  Trevor Hastie,et al.  Regularized linear discriminant analysis and its application in microarrays. , 2007, Biostatistics.

[8]  Kourosh Mozafari,et al.  Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA) , 2012, Comput. Methods Programs Biomed..

[9]  David A. Elizondo,et al.  Linear separability and classification complexity , 2012, Expert Syst. Appl..

[10]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[11]  Kai Li,et al.  Efficient k-nearest neighbor graph construction for generic similarity measures , 2011, WWW.

[12]  K. Revett,et al.  Mining A Primary Biliary Cirrhosis Dataset Using Rough Sets and a Probabilistic Neural Network , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[13]  Pasi Luukka,et al.  Classification based on fuzzy robust PCA algorithms and similarity classifier , 2009, Expert Syst. Appl..

[14]  Yogesh Kumar Dwivedi,et al.  Handbook of Research on Advances in Health Informatics and Electronic Healthcare Applications: Global Adoption and Impact of Information Communication Technologies , 2009 .

[15]  Oscar Camacho Nieto,et al.  An associative memory approach to medical decision support systems , 2012, Comput. Methods Programs Biomed..

[16]  M. Cruz-cunha,et al.  Handbook of Research on Developments in E-health and Telemedicine: Technological and Social Perspectives , 2009 .

[17]  Der-Chiang Li,et al.  A class possibility based kernel to increase classification accuracy for small data sets using support vector machines , 2010, Expert Syst. Appl..

[18]  Kemal Polat,et al.  A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing , 2007, Expert Syst. Appl..

[19]  Shiliang Sun,et al.  An adaptive k-nearest neighbor algorithm , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[20]  Sung-Bae Cho,et al.  Evolutionarily optimized features in functional link neural network for classification , 2010, Expert Syst. Appl..

[21]  I. O. Bucak,et al.  Diagnosis of liver disease by using CMAC neural network approach , 2010, Expert Syst. Appl..

[22]  Rong-Ho Lin,et al.  An intelligent model for liver disease diagnosis , 2009, Artif. Intell. Medicine.

[23]  Martti Juhola,et al.  On the neural network classification of medical data and an endeavour to balance non-uniform data sets with artificial data extension , 2007, Comput. Biol. Medicine.

[24]  Pau-Choo Chung,et al.  Classification of liver diseases from CT images using BP-CMAC neural network , 2005, 2005 9th International Workshop on Cellular Neural Networks and Their Applications.

[25]  Katsumi Yoshida,et al.  A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders , 2000, Artif. Intell. Medicine.

[26]  Olfat G. Shaker,et al.  Prediction of the degree of liver fibrosis using different pattern recognition techniques , 2010, 2010 5th Cairo International Biomedical Engineering Conference.

[27]  Lale Özbakir,et al.  Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks , 2013, Expert Syst. Appl..

[28]  Yunis Torun,et al.  Designing simulated annealing and subtractive clustering based fuzzy classifier , 2011, Appl. Soft Comput..

[29]  Shigeo Abe,et al.  Fuzzy least squares support vector machines for multiclass problems , 2003, Neural Networks.

[30]  Kemal Polat,et al.  Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism , 2007, Expert Syst. Appl..

[31]  Novruz Allahverdi,et al.  Extracting rules for classification problems: AIS based approach , 2009, Expert Syst. Appl..

[32]  Mehdi Neshat,et al.  Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders , 2010, 2010 5th IEEE International Conference Intelligent Systems.

[33]  Chun-Ling Chuang,et al.  Case-based reasoning support for liver disease diagnosis , 2011, Artif. Intell. Medicine.

[34]  Ma Lizhuang,et al.  Correlation between Child-Pugh Degree and the Four Examinations of Traditional Chinese Medicine (TCM) with Liver Cirrhosis , 2008, BMEI 2008.

[35]  Olgierd Unold,et al.  Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning , 2011, Appl. Soft Comput..

[36]  Samuel S. Udoh,et al.  A framework for fuzzy diagnosis of hepatitis , 2011, 2011 World Congress on Information and Communication Technologies.

[37]  Tulay Yildirim,et al.  Artificial neural networks for diagnosis of hepatitis disease , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[38]  Smaranda Belciug,et al.  Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network , 2012, Expert Syst. Appl..

[39]  Hanan Samet,et al.  K-Nearest Neighbor Finding Using MaxNearestDist , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Loo Chu Kiong,et al.  Autonomous and deterministic supervised fuzzy clustering with data imputation capabilities , 2011 .

[41]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[42]  Jieping Ye,et al.  A two-stage linear discriminant analysis via QR-decomposition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Der-Chiang Li,et al.  A learning method for the class imbalance problem with medical data sets , 2010, Comput. Biol. Medicine.

[44]  Sadik Kara,et al.  Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease , 2006, Expert Syst. Appl..

[45]  Babita Pandey,et al.  Intelligent techniques and applications in liver disorders: a survey , 2014 .

[46]  H. Maeta,et al.  Prediction of the early prognosis of the hepatectomized patient with hepatocellular carcinoma with a neural network. , 1995, Computers in biology and medicine.

[47]  Chun-Ling Chuang,et al.  A hybrid diagnosis model for determining the types of the liver disease , 2010, Comput. Biol. Medicine.

[48]  C. Stephenson,et al.  A process architecture approach to manage health process reforms , 2010 .