Intelligent techniques and applications in liver disorders: a survey

Liver disease is one of the leading causes of mortality in India, as it is in rest of the world. This paper presents a survey on intelligent techniques applied to liver disorders between the years January 1995 and January 2013. Individual ITs include artificial neural network (ANN), data mining (DM), fuzzy logic (FL) etc. Integrated ITs combine methods as artificial neural network-case-based reasoning (ANN-CBR), artificial immune system-artificial neural network-fuzzy logic (AIS-ANN-FL) etc. The different types of liver disorders covered in the study are hepatitis, liver fibrosis, liver cirrhosis, liver cancer, fatty liver, liver disorders data set, hepatitis data set and hepatobiliary disorders data set. The study identifies which ITs are applied for what types of liver disorders and on which types of disorders maximum works has been done. Another imperative fact emerging from this survey is that large part of the research work on liver disorders has been done from 2007 onwards.

[1]  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.

[2]  M. Neshat,et al.  Fuzzy Expert System Design for Diagnosis of Liver Disorders , 2008, 2008 International Symposium on Knowledge Acquisition and Modeling.

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

[4]  Sultan Noman Qasem,et al.  Author's Personal Copy Applied Soft Computing Radial Basis Function Network Based on Time Variant Multi-objective Particle Swarm Optimization for Medical Diseases Diagnosis , 2022 .

[5]  Yen-Wei Chen,et al.  Application of statistical shape model to diagnosis of liver disease , 2010, The 2nd International Conference on Software Engineering and Data Mining.

[6]  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.

[7]  Hong Zhou,et al.  Application of data mining technology in excavating prevention and treatment experience of infectious diseases from famous herbalist doctors , 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[8]  Seral Özsen,et al.  Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems , 2009, Expert Syst. Appl..

[9]  Esin Dogantekin,et al.  Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System , 2009, Expert Syst. Appl..

[10]  Pei-Chann Chang,et al.  A CBR-based fuzzy decision tree approach for database classification , 2010, Expert Syst. Appl..

[11]  Paulo J. G. Lisboa,et al.  Bayesian Neural Network Applied in Medical Survival Analysis of Primary Biliary Cirrhosis , 2012, 2012 UKSim 14th International Conference on Computer Modelling and Simulation.

[12]  Yong Man Ro,et al.  Multiple ROI selection based focal liver lesion classification in ultrasound images , 2013, Expert Syst. Appl..

[13]  Guido Bologna,et al.  A model for single and multiple knowledge based networks , 2003, Artif. Intell. Medicine.

[14]  Modjtaba Rouhani,et al.  The Diagnosis of Hepatitis Diseases by Support Vector Machines and Artificial Neural Networks , 2009, 2009 International Association of Computer Science and Information Technology - Spring Conference.

[15]  Yüksel Özbay,et al.  Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease , 2011, Expert Syst. Appl..

[16]  Kemal Polat,et al.  Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection , 2007, Expert Syst. Appl..

[17]  Bernard C. Jiang,et al.  Application of classification techniques on development an early-warning system for chronic illnesses , 2012, Expert Syst. Appl..

[18]  A.A. Ghatol,et al.  Hepatitis B Diagnosis Using Logical Inference And Generalized Regression Neural Networks , 2009, 2009 IEEE International Advance Computing Conference.

[19]  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..

[20]  Kwong-Sak Leung,et al.  Data Mining on DNA Sequences of Hepatitis B Virus , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[21]  Pasi Luukka.  Fuzzy beans in classification , 2011, Expert Syst. Appl..

[22]  Ruxandra Stoean,et al.  Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C , 2011, Artif. Intell. Medicine.

[23]  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.

[24]  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..

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

[26]  Der-Chiang Li,et al.  A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets , 2011, Artif. Intell. Medicine.

[27]  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..

[28]  K. S. Chaudhuri,et al.  genetic algorithm-based rule extraction system ikash , 2011 .

[29]  Yang Zhang,et al.  A generic optimising feature extraction method using multiobjective genetic programming , 2011, Appl. Soft Comput..

[30]  Bogdan Gabrys,et al.  Generalised bottom-up pruning: A model level combination of decision trees , 2012, Expert Syst. Appl..

[31]  Nafisa Afrin Chowdhury,et al.  On integrating fuzzy knowledge using a Novel Evolutionary Algorithm , 2007, 2007 10th international conference on computer and information technology.

[32]  Ruxandra Stoean,et al.  Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis , 2011, Comput. Biol. Medicine.

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

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

[35]  Pei-Chann Chang,et al.  An attribute weight assignment and particle swarm optimization algorithm for medical database classifications , 2012, Comput. Methods Programs Biomed..

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

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

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

[39]  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..

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

[41]  Peter Z. Revesz,et al.  Classification integration and reclassification using constraint databases , 2010, Artif. Intell. Medicine.

[42]  Chao-Ton Su,et al.  Feature selection for the SVM: An application to hypertension diagnosis , 2008, Expert Syst. Appl..

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

[44]  Ahmed M. Hashem,et al.  Single stage and multistage classification models for the prediction of liver fibrosis degree in patients with chronic hepatitis C infection , 2012, Comput. Methods Programs Biomed..

[45]  Sundaram Suresh,et al.  Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems , 2013, Appl. Soft Comput..

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

[47]  Yuehwern Yih,et al.  Knowledge acquisition through information granulation for imbalanced data , 2006, Expert Syst. Appl..

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

[49]  Tzung-Pei Hong,et al.  Integrating fuzzy knowledge by genetic algorithms , 1998, IEEE Trans. Evol. Comput..

[50]  Ludmil Mikhailov,et al.  An interpretable fuzzy rule-based classification methodology for medical diagnosis , 2009, Artif. Intell. Medicine.

[51]  Yan Wang,et al.  Correlation between Child-Pugh Degree and the Four Examinations of Traditional Chinese Medicine (TCM) with Liver Cirrhosis , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

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

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

[54]  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.

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

[56]  Jamil Ahmad,et al.  Diagnosis of liver disease induced by hepatitis virus using Artificial Neural Networks , 2011, 2011 IEEE 14th International Multitopic Conference.

[57]  Ivanoe De Falco,et al.  Differential Evolution for automatic rule extraction from medical databases , 2013, Appl. Soft Comput..

[58]  Yoichi Hayashi,et al.  Combining neural network predictions for medical diagnosis , 2002, Comput. Biol. Medicine.

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

[60]  Se-Hak Chun,et al.  Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis , 2011, Artif. Intell. Medicine.

[61]  César Hervás-Martínez,et al.  Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants , 2012, Eur. J. Oper. Res..

[62]  Ahmed M. Badawi,et al.  Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images , 1999, Int. J. Medical Informatics.

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

[64]  Esin Dogantekin,et al.  A new intelligent hepatitis diagnosis system: PCA-LSSVM , 2011, Expert Syst. Appl..

[65]  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.

[66]  Pasi Luukka,et al.  Similarity classifier with generalized mean applied to medical data , 2006, Comput. Biol. Medicine.

[67]  Okure Udo Obot,et al.  A framework for application of neuro-case-rule base hybridization in medical diagnosis , 2009, Appl. Soft Comput..

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

[69]  Zhonghang Xia,et al.  A two-level approach to choose the cost parameter in support vector machines , 2008, Expert Syst. Appl..

[70]  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.

[71]  Jianming Lu,et al.  Ultrasonographic classification of cirrhosis based on pyramid neural network , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

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

[73]  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..

[74]  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..

[75]  Tong Heng Lee,et al.  Evolutionary computing for knowledge discovery in medical diagnosis , 2003, Artif. Intell. Medicine.

[76]  V. Sadasivam,et al.  Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network , 2005, 2005 Annual IEEE India Conference - Indicon.

[77]  Pei-Chann Chang,et al.  A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification , 2011, Appl. Soft Comput..

[78]  Kemal Polat,et al.  A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS , 2007, Comput. Methods Programs Biomed..

[79]  Yung-Chang Chen,et al.  Ultrasonic liver tissue characterization by feature fusion , 2012, Expert Syst. Appl..

[80]  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.

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

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