Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates
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Tarik A. Rashid | Mehrbakhsh Nilashi | Hossein Ahmadi | Nahla Aljojo | Leila Shahmoradi | Sarminah Samad | Azizah Abdul Manaf | Elnaz Akbari | M. Nilashi | Sarminah Samad | N. Aljojo | E. Akbari | H. Ahmadi | L. Shahmoradi | A. Manaf | Tarik A. Rashid | Hossein Ahmadi
[1] Esa Alhoniemi,et al. Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..
[2] Sheng-De Wang,et al. Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.
[3] A. Maćkiewicz,et al. Principal Components Analysis (PCA) , 1993 .
[4] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[5] John Wright,et al. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.
[6] Mehrbakhsh Nilashi,et al. Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach , 2019 .
[7] Yi-Ping Phoebe Chen,et al. Association rule mining to detect factors which contribute to heart disease in males and females , 2013, Expert Syst. Appl..
[8] Thorsten Joachims,et al. Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.
[9] Mehrbakhsh Nilashi,et al. A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques , 2017 .
[10] Kapil Wankhade,et al. Decision support system for heart disease based on support vector machine and Artificial Neural Network , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).
[11] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .
[12] Abdulkadir Sengür,et al. Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..
[13] Chris H. Q. Ding,et al. K-means clustering via principal component analysis , 2004, ICML.
[14] Roderick J A Little,et al. A Review of Hot Deck Imputation for Survey Non‐response , 2010, International statistical review = Revue internationale de statistique.
[15] Payman Moallem,et al. Automatic Detection of Malignant Melanoma using Macroscopic Images , 2014, Journal of medical signals and sensors.
[16] Yifan Sun,et al. Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments , 2019, Expert Syst. Appl..
[17] Ernestina Menasalvas Ruiz,et al. Supervoxels-Based Histon as a New Alzheimer’s Disease Imaging Biomarker , 2018, Sensors.
[18] Biswajeet Pradhan,et al. Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods , 2018 .
[19] H. Abdi,et al. Principal component analysis , 2010 .
[20] E. Yadegaridehkordi,et al. Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels , 2019, Journal of Retailing and Consumer Services.
[21] AbdiHervé,et al. Principal Component Analysis , 2010, Essentials of Pattern Recognition.
[22] Hong Gu,et al. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. , 2016, Journal of theoretical biology.
[23] D Haussler,et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[24] Elif Derya Übeyli,et al. Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering , 2010, Journal of Medical Systems.
[25] Ashok Ghatol,et al. Feature selection for medical diagnosis : Evaluation for cardiovascular diseases , 2013, Expert Syst. Appl..
[26] Omar H. Karam,et al. Feature Analysis of Coronary Artery Heart Disease Data Sets , 2015 .
[27] Dae-Ki Kang,et al. Optimizing SVM Ensembles Using Genetic Algorithms in Bankruptcy Prediction , 2010, J. Inform. and Commun. Convergence Engineering.
[28] Mehrbakhsh Nilashi,et al. Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach , 2019, Comput. Ind. Eng..
[29] Wenjian Wang,et al. Online prediction model based on support vector machine , 2008, Neurocomputing.
[30] D. Chen,et al. Breast cancer diagnosis using self-organizing map for sonography. , 2000, Ultrasound in medicine & biology.
[31] M. Elgamal,et al. The relation between hepatitis C virus and coronary heart disease. , 2014, Medical hypotheses.
[32] B. Norrving,et al. Global atlas on cardiovascular disease prevention and control. , 2011 .
[33] M. Hariharan,et al. A new hybrid intelligent system for accurate detection of Parkinson's disease , 2014, Comput. Methods Programs Biomed..
[34] Sheeraz Akram,et al. Heart disease classification ensemble optimization using Genetic algorithm , 2011, 2011 IEEE 14th International Multitopic Conference.
[35] Mehrbakhsh Nilashi,et al. A knowledge-based system for breast cancer classification using fuzzy logic method , 2017, Telematics Informatics.
[36] K. R. Al-Balushi,et al. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .
[37] R. Haynes,et al. Effects of Computer-based Clinical Decision Support Systems on Clinician Performance and Patient Outcome: A Critical Appraisal of Research , 1994, Annals of Internal Medicine.
[38] Teresa A. Myers. Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data , 2011 .
[39] David West,et al. A comparison of SOM neural network and hierarchical clustering methods , 1996 .
[40] Mehrbakhsh Nilashi,et al. Accuracy Improvement for Predicting Parkinson’s Disease Progression , 2016, Scientific Reports.
[41] S. Gunn. Support Vector Machines for Classification and Regression , 1998 .
[42] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[43] Neda Ahmadi,et al. An intelligent method for iris recognition using supervised machine learning techniques , 2019 .
[44] Harichandran Khanna Nehemiah,et al. Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network , 2015, Comput. Math. Methods Medicine.
[45] Liyana Shuib,et al. The impact of big data on firm performance in hotel industry , 2020, Electron. Commer. Res. Appl..
[46] Abdeltawab M. Hendawi,et al. Heart disease identification from patients' social posts, machine learning solution on Spark , 2020, Future Gener. Comput. Syst..
[47] Novruz Allahverdi,et al. Design of a hybrid system for the diabetes and heart diseases , 2008, Expert Syst. Appl..
[48] J. Leal,et al. UK research expenditure on dementia, heart disease, stroke and cancer: are levels of spending related to disease burden? , 2012, European journal of neurology.
[49] Luis Mateus Rocha,et al. Singular value decomposition and principal component analysis , 2003 .
[50] Mehrbakhsh Nilashi,et al. An analytical method for diseases prediction using machine learning techniques , 2017, Comput. Chem. Eng..
[51] Kimmo Kiviluoto,et al. Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.
[52] Markus Ringnér,et al. What is principal component analysis? , 2008, Nature Biotechnology.
[53] Mehrbakhsh Nilashi,et al. A Soft Computing Method for Mesothelioma Disease Classification , 2017 .
[54] Nigel Stallard,et al. The changing face of cardiovascular disease 2000-2012: An analysis of the world health organisation global health estimates data. , 2016, International journal of cardiology.
[55] N. Mantel. The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.
[56] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[57] R. Cattell. The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.
[58] T. Santhanam,et al. Application of K-Means and Genetic Algorithms for Dimension Reduction by Integrating SVM for Diabetes Diagnosis , 2015 .
[59] Hong Shen,et al. Application of online-training SVMs for real-time intrusion detection with different considerations , 2005, Comput. Commun..
[60] Barbara Caputo,et al. Recognizing human actions: a local SVM approach , 2004, ICPR 2004.
[61] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[62] Chih-Chou Chiu,et al. Hybrid intelligent modeling schemes for heart disease classification , 2014, Appl. Soft Comput..
[63] Francisco Jesús Martínez-Murcia,et al. LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease , 2013, Pattern Recognit. Lett..
[64] Sang Won Yoon,et al. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..
[65] H. Fowler,et al. Sensitivity of extreme rainfall to temperature in semi-arid Mediterranean regions , 2019, Atmospheric Research.
[66] T. Kohonen. Analysis of a simple self-organizing process , 1982, Biological Cybernetics.
[67] Kazuyuki Murase,et al. Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease , 2018, Applied Intelligence.
[68] Ohbyung Kwon,et al. Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications , 2015 .
[69] P. K. Anooj,et al. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules , 2012, J. King Saud Univ. Comput. Inf. Sci..
[70] Mehrbakhsh Nilashi,et al. An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset , 2019, Measurement.
[71] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[72] Kindie Biredagn Nahato,et al. Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets , 2016 .
[73] Eta S. Berner,et al. Clinical Decision Support Systems , 1999, Health Informatics.
[74] Ana M. Aguilera,et al. Using principal components for estimating logistic regression with high-dimensional multicollinear data , 2006, Comput. Stat. Data Anal..
[75] Mehrbakhsh Nilashi,et al. Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset , 2017 .
[76] A. Mechelli,et al. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.