Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.

[1]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[2]  Rob Stocker,et al.  Using Decision Tree for Diagnosing Heart Disease Patients , 2011, AusDM.

[3]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[4]  Nikola K. Kasabov,et al.  Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes , 2015, Inf. Sci..

[5]  Kay Chen Tan,et al.  A hybrid evolutionary algorithm for attribute selection in data mining , 2009, Expert Syst. Appl..

[6]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  G. Pillai,et al.  SVM Based Decision Support System for Heart Disease Classification with Integer-Coded Genetic Algorithm to Select Critical Features , 2009 .

[9]  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..

[10]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[11]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[12]  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..

[13]  Lukasz Kurgan,et al.  Data Mining and Knowledge Discovery Data Mining and Knowledge Discovery , 2002 .

[14]  Jun Wang,et al.  Research on C5.0 Algorithm Improvement and the Test in Lightning Disaster Statistics , 2014 .

[15]  Pankaj Kumar,et al.  EARLY HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES , 2014, CSE 2014.

[16]  Peter C Austin,et al.  Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. , 2013, Journal of clinical epidemiology.

[17]  Kyle E. Walker,et al.  Classifying high-prevalence neighborhoods for cardiovascular disease in Texas , 2015 .

[18]  Sarinder Kaur A P Kashmir Singh.  A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease , 2014 .

[19]  Jafar Habibi,et al.  Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features , 2013, Research in cardiovascular medicine.

[20]  Amandeep S. Sidhu,et al.  A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease , 2014 .

[21]  T. John Peter,et al.  Study and Development of Novel Feature Selection Framework for Heart Disease Prediction , 2012 .

[22]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[23]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[26]  A. Senthil Kumar.  Generating Rules for Advanced Fuzzy Resolution Mechanism to Diagnosis Heart Disease , 2013 .

[27]  E. A. Stolz,et al.  Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks1 , 1998 .

[28]  Yongqiang Lyu,et al.  Dynamic evaluation model of coronary heart disease for ubiquitous healthcare , 2015, Comput. Ind..

[29]  T. Martin McGinnity,et al.  Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms , 2006, IEEE Transactions on Fuzzy Systems.

[30]  Touradj Ebrahimi,et al.  Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[31]  M. Tech,et al.  Decision Support in Heart Disease Prediction System using Naive Bayes , 2011 .

[32]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[33]  Usman Qamar,et al.  MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble , 2014 .

[34]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[35]  T. R. Neelakantan,et al.  Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks , 2012 .

[36]  Hamid Bagheri,et al.  Big Data: Challenges, Opportunities and Cloud Based Solutions , 2015 .

[37]  Charles W. Anderson.  Clinical applications of artificial neural networks: Recent advances in EEG signal analysis and classification , 2001 .

[38]  K.Sri Ramakrishna,et al.  Detection of Atrial Fibrillation using Autoregressive modeling , 2015 .