A systematic mapping study for ensemble classification methods in cardiovascular disease

[1]  Abdeltawab M. Hendawi,et al.  Heart disease identification from patients' social posts, machine learning solution on Spark , 2020, Future Gener. Comput. Syst..

[2]  Shailendra Narayan Singh,et al.  Data mining classification techniques - comparison for better accuracy in prediction of cardiovascular disease , 2019, Int. J. Data Anal. Tech. Strateg..

[3]  Christophe Nicolle,et al.  Agent-based simulation of unmanned aerial vehicles in civilian applications: A systematic literature review and research directions , 2019, Future Gener. Comput. Syst..

[4]  Ali Idri,et al.  Systematic mapping study of data mining–based empirical studies in cardiology , 2019, Health Informatics J..

[5]  Samir Chatterjee,et al.  Designing a Machine Learning Model to Predict Cardiovascular Disease Without Any Blood Test , 2019, DESRIST.

[6]  Ali Idri,et al.  Impact of Parameter Tuning on Machine Learning Based Breast Cancer Classification , 2019, WorldCIST.

[7]  Nadeem Akhtar,et al.  Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases , 2019 .

[8]  Lenka Lhotska.  World Congress on Medical Physics and Biomedical Engineering 2018, June 3-8, 2018, Prague, Czech Republic (Vol. 1) , 2018 .

[9]  Alain Abran,et al.  On the value of parameter tuning in heterogeneous ensembles effort estimation , 2017, Soft Computing.

[10]  Yang Zhang,et al.  A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State , 2018, Journal of clinical medicine.

[11]  Abdelhamid Mellouk,et al.  An Enhanced Random Forest for Cardiac Diseases Identification based on ECG signal , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[12]  Panagiota I. Tsompou,et al.  A Novel Concept of the Management of Coronary Artery Disease Patients Based on Machine Learning Risk Stratification and Computational Biomechanics: Preliminary Results of SMARTool Project , 2018, IFMBE Proceedings.

[13]  Prashant Warier,et al.  Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients , 2018, Journal of the American Heart Association.

[14]  Amit Kumar,et al.  A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets , 2018, J. Inf. Technol. Res..

[15]  S. M. M. Hasan,et al.  Comparative Analysis of Classification Approaches for Heart Disease Prediction , 2018, 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2).

[16]  Pål Aukrust,et al.  Increased Levels of Lectin‐Like Oxidized Low‐Density Lipoprotein Receptor‐1 in Ischemic Stroke and Transient Ischemic Attack , 2018, Journal of the American Heart Association.

[17]  Sang Won Yoon,et al.  A support vector machine-based ensemble algorithm for breast cancer diagnosis , 2017, Eur. J. Oper. Res..

[18]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[19]  Ali Idri,et al.  Software Development Effort Estimation Using Feature Selection Techniques , 2018, New Trends in Software Methodologies, Tools and Techniques.

[20]  Elham Nikookar,et al.  Hybrid Ensemble Framework for Heart Disease Detection and Prediction , 2018 .

[21]  Alberto Palacios Pawlovsky,et al.  An ensemble based on distances for a kNN method for heart disease diagnosis , 2018, 2018 International Conference on Electronics, Information, and Communication (ICEIC).

[22]  Syed Muhammad Anwar,et al.  A statistical analysis based recommender model for heart disease patients , 2017, Int. J. Medical Informatics.

[23]  Suphakant Phimoltares,et al.  Diagnosis of Heart Disease Using a Mixed Classifier , 2017, 2017 21st International Computer Science and Engineering Conference (ICSEC).

[24]  Alain Abran,et al.  Investigating heterogeneous ensembles with filter feature selection for software effort estimation , 2017, IWSM-Mensura.

[25]  B. C. Loh,et al.  Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. , 2017, mHealth.

[26]  Yang Zhang,et al.  Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[27]  Mehmet Kuntalp,et al.  Paroxysmal atrial fibrillation (PAF) screening by ensemble learning , 2017, 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE).

[28]  Dawid Smolen.  Atrial fibrillation detection using boosting and stacking ensemble , 2017, 2017 Computing in Cardiology (CinC).

[29]  Andreas K. Maier,et al.  Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring , 2017, MLMI@MICCAI.

[30]  Talal Shaikh,et al.  Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest , 2017, 2017 International Conference on Computer and Applications (ICCA).

[31]  Yao Xiao,et al.  RFMiner: Risk Factors Discovery and Mining for Preventive Cardiovascular Health , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[32]  Yanbo Zhang,et al.  Predicting congenital heart defects: A comparison of three data mining methods , 2017, PloS one.

[33]  Ji Zhang,et al.  A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset , 2017, PAKDD.

[34]  Arpan Pal,et al.  A fusion approach for non-invasive detection of coronary artery disease , 2017, PervasiveHealth.

[35]  Fulong Chen,et al.  Coupling a Fast Fourier Transformation With a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment , 2017, IEEE Access.

[36]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[37]  Ali Idri,et al.  Software effort estimation using classical analogy ensembles based on random subspace , 2017, SAC.

[38]  Cyril Ferdynus,et al.  A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis , 2017, PloS one.

[39]  Ali Idri,et al.  Knowledge discovery in cardiology: A systematic literature review , 2017, Int. J. Medical Informatics.

[40]  Vidya K. Sudarshan,et al.  Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[41]  Xuhui Chen,et al.  Real-time personalized cardiac arrhythmia detection and diagnosis: A cloud computing architecture , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[42]  Alain Abran,et al.  Improved estimation of software development effort using Classical and Fuzzy Analogy ensembles , 2016, Appl. Soft Comput..

[43]  Usman Qamar,et al.  A Multicriteria Weighted Vote‐Based Classifier Ensemble for Heart Disease Prediction , 2016, Comput. Intell..

[44]  Alain Abran,et al.  Heterogeneous Ensembles for Software Development Effort Estimation , 2016, 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI).

[45]  Burak Kantarci,et al.  Machine Learning in Cardiac Health Monitoring and Decision Support , 2016, Computer.

[46]  Yorgos Goletsis,et al.  Predicting adherence of patients with HF through machine learning techniques. , 2016, Healthcare technology letters.

[47]  Domenico Conforti,et al.  Machine learning approaches for supporting patient-specific cardiac rehabilitation programs , 2016, 2016 Computing in Cardiology Conference (CinC).

[48]  Bryan R. Conroy,et al.  Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds , 2016, 2016 Computing in Cardiology Conference (CinC).

[49]  Anna Rosiek,et al.  The risk factors and prevention of cardiovascular disease: the importance of electrocardiogram in the diagnosis and treatment of acute coronary syndrome , 2016, Therapeutics and clinical risk management.

[50]  Alain Abran,et al.  Systematic literature review of ensemble effort estimation , 2016, J. Syst. Softw..

[51]  Regina Berretta,et al.  Optimising weights for heterogeneous ensemble of classifiers with differential evolution , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[52]  Randa El Bialy,et al.  An ensemble model for Heart disease data sets: a generalized model , 2016, INFOS '16.

[53]  Deepa Gupta,et al.  A method to predict diagnostic codes for chronic diseases using machine learning techniques , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[54]  Ali Idri,et al.  Systematic Mapping Study of Ensemble Effort Estimation , 2016, ENASE.

[55]  Oliver Faust,et al.  Computer aided diagnosis for cardiovascular diseases based on ECG signals : a survey , 2016 .

[56]  Usman Qamar,et al.  IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework , 2016, J. Biomed. Informatics.

[57]  O. S. Deepa,et al.  Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors , 2016 .

[58]  Ethem Alpaydin,et al.  Bagging Soft Decision Trees , 2016, Machine Learning for Health Informatics.

[59]  Jemal H. Abawajy,et al.  Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles , 2016, IEEE Journal of Biomedical and Health Informatics.

[60]  Muhammad Arif,et al.  Decision Trees Based Classification of Cardiotocograms Using Bagging Approach , 2015, 2015 13th International Conference on Frontiers of Information Technology (FIT).

[61]  José David Martín-Guerrero,et al.  Improving Mortality Prediction in Cardiovascular Risk Patients by Balancing Classes , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[62]  Ernesto Iadanza,et al.  A multi-layer monitoring system for clinical management of Congestive Heart Failure , 2015, BMC Medical Informatics and Decision Making.

[63]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[64]  Kai Petersen,et al.  Guidelines for conducting systematic mapping studies in software engineering: An update , 2015, Inf. Softw. Technol..

[65]  Abdulhamit Subasi,et al.  Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques , 2015, Appl. Soft Comput..

[66]  Nan Liu,et al.  Analysis of patient outcome using ECG and extreme learning machine ensemble , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[67]  Paolo Melillo,et al.  Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects. , 2015, Healthcare technology letters.

[68]  M. Chikh,et al.  Classifier Set Selection for Cardiac Arrhythmia Recognition Using Diversity , 2015 .

[69]  Usman Qamar,et al.  BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting , 2015, Australasian Physical & Engineering Sciences in Medicine.

[70]  Venkatesh Saligrama,et al.  Prediction of hospitalization due to heart diseases by supervised learning methods , 2015, Int. J. Medical Informatics.

[71]  Khin Mo Mo Tun,et al.  AN APPROACH FOR BREAST CANCER DIAGNOSIS CLASSIFICATION USING NEURAL NETWORK , 2015 .

[72]  L. V. Nandakishore,et al.  Ensemble Neural Network Algorithm for Detecting Cardiac Arrhythmia , 2015 .

[73]  Bulusu Lakshmana Deekshatulu,et al.  Prediction of Heart Disease Using Random Forest and Feature Subset Selection , 2015, IBICA.

[74]  Xiaoqing Luo,et al.  Heartbeat classification using decision level fusion , 2014 .

[75]  Gabriele Guidi,et al.  A Machine Learning System to Improve Heart Failure Patient Assistance , 2014, IEEE Journal of Biomedical and Health Informatics.

[76]  Alípio Mário Jorge,et al.  Classifying heart sounds using SAX motifs, random forests and text mining techniques , 2014, IDEAS.

[77]  Amir-Masoud Eftekhari-Moghadam,et al.  Knowledge discovery in medicine: Current issue and future trend , 2014, Expert Syst. Appl..

[78]  Alan Wee-Chung Liew,et al.  A novel genetic algorithm approach for simultaneous feature and classifier selection in multi classifier system , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[79]  Ulrich Parlitz,et al.  Evaluation of machine learning methods for the long-term prediction of cardiac diseases , 2014, 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO).

[80]  Li Zhang,et al.  An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).

[81]  Sanjay L. Nalbalwar,et al.  Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis , 2013, Soft Computing.

[82]  Mehrdad Javadi,et al.  Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats , 2013, Journal of medical engineering & technology.

[83]  Ankur Teredesai,et al.  Big data solutions for predicting risk-of-readmission for congestive heart failure patients , 2013, 2013 IEEE International Conference on Big Data.

[84]  Tarek Helmy,et al.  Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation , 2013 .

[85]  S. Geetha,et al.  An efficient feature selection paradigm using PCA-CFS-Shapley values ensemble applied to small medical data sets , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[86]  Yorgos Goletsis,et al.  Adverse event prediction in patients with left ventricular assist devices , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[87]  Jafar Habibi,et al.  A data mining approach for diagnosis of coronary artery disease , 2013, Comput. Methods Programs Biomed..

[88]  Bartosz Krawczyk,et al.  On optimal settings of classification tree ensembles for medical decision support , 2013, Health Informatics J..

[89]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2013, JAMA.

[90]  N. Jaisankar,et al.  Comprehensive Study of Heart Disease Diagnosis Using Data Mining and Soft Computing Techniques , 2013 .

[91]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[92]  K. Usha Rani,et al.  ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA , 2012 .

[93]  Yong Hu,et al.  Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..

[94]  Manjula Sanjay Koti,et al.  A Comparison of different learning models used in Data Mining for Medical Data , 2011 .

[95]  Huihui Zhao,et al.  Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by the T-Test-Based Adaboost Algorithm , 2011, Evidence-based complementary and alternative medicine : eCAM.

[96]  Fai Wong,et al.  Ensemble learning on heartbeat type classification , 2011, Proceedings 2011 International Conference on System Science and Engineering.

[97]  Huifang Huang,et al.  Ensemble of support vector machines for heartbeat classification , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[98]  Sanjay L. Nalbalwar,et al.  ECG arrhythmia classification using modular neural network model , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[99]  E. Tripoliti,et al.  Knowledge extraction in a population suffering from heart failure , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[100]  Giovanni Seni,et al.  Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions , 2010, Ensemble Methods in Data Mining.

[101]  Wenhuang Liu,et al.  Dynamic Weighting Ensembles for Incremental Learning , 2009, 2009 Chinese Conference on Pattern Recognition.

[102]  Dongkyoo Shin,et al.  Effective Diagnosis of Heart Disease through Bagging Approach , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[103]  Fei Su,et al.  Face recognition using SURF features , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[104]  Xin Yao,et al.  Diversity analysis on imbalanced data sets by using ensemble models , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[105]  Abdulkadir Sengür,et al.  Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..

[106]  Abdulkadir Sengür,et al.  Diagnosis of valvular heart disease through neural networks ensembles , 2009, Comput. Methods Programs Biomed..

[107]  José Hernández-Orallo,et al.  An experimental comparison of performance measures for classification , 2009, Pattern Recognit. Lett..

[108]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[109]  Byoung-Tak Zhang,et al.  AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction , 2008, Expert Syst. Appl..

[110]  T. Ichida.  World Medical Association declaration of Helsinki , 1991, Gastroenterologia Japonica.

[111]  Chao Tan,et al.  The Prediction of Cardiovascular Disease Based on Trace Element Contents in Hair and a Classifier of Boosting Decision Stumps , 2008, Biological Trace Element Research.

[112]  Doo-Hwan Bae,et al.  Systematic Functional Decomposition in a Product Line Using Aspect-oriented Software Development: a Case Study , 2007, Int. J. Softw. Eng. Knowl. Eng..

[113]  Wang Yong,et al.  A Better Classifier Based on Rough Set and Neural Network for Medical Images , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[114]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[115]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[116]  Jorma Laaksonen,et al.  Using diversity of errors for selecting members of a committee classifier , 2006, Pattern Recognit..

[117]  Rosa Maria Valdovinos,et al.  Class-dependant resampling for medical applications , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[118]  Roel Wieringa,et al.  Requirements engineering paper classification and evaluation criteria: a proposal and a discussion , 2005, Requirements Engineering.

[119]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

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

[121]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[122]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[123]  N. N. Available.  World medical association declaration of Helsinki , 2000, Chinese Journal of Integrative Medicine.

[124]  C. J. Whitaker,et al.  Ten measures of diversity in classifier ensembles: limits for two classifiers , 2001 .

[125]  D. Ruta,et al.  An Overview of Classifier Fusion Methods , 2000 .

[126]  Chee Peng Lim,et al.  An experimental study of original and ordered fuzzy ARTMAP neural networks in pattern classification tasks , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[127]  C. Faloutsos,et al.  Ensemble Methods , 2019, Machine Learning with Spark™ and Python®.

[128]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[129]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[130]  OpitzDavid,et al.  Popular ensemble methods , 1999 .

[131]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[132]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[133]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[134]  Robert E. Schapire,et al.  The Strength of Weak Learnability , 1989, 30th Annual Symposium on Foundations of Computer Science.

[135]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.