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Heart Disease Data Mining Using Classification Techniques

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INTRODUCTION METHODOLOGY REFERENCESCONCLUSION RESULT RELATED WORK
PAPER REVIEW HEART DISEASE DATA MINING
EXPERIMENTAL SETUP DATA MINING TECHNIQUES PERCEPTRON & SOFTWARE
BEGIN DATASETS 10-FOLD CROSS VALIDATION SELECTION CLASSIFIER SELECTION KSTAR J84 SMO BAYES NET MLP PERFORMENCE ANALYSIS OF THE SELECTED CLASSIFIER END
TECHNIQUES MLP (Multi-layer Perceptron) Decision Tree Bayes Net KStar SMO (Sequential Minimal Optimization)
PERCEPTRON WEKA
Classification Collected Data Standard Data Accuracy Model Building Time (s) Accuracy Model Building Time (s) KStar 75% 0 75.1852% 0 J84 86% 0 76.6667% 0 SMO 89% 0 84.0741% 0.02 Bayes Net 87% 0 81.1111% 0.02 MLP 86% 0.33 77.4074% 0.75 TABLE FOR PERFORMANCE MEASURE OF CLASSIFIERS
Comparative study of accuracy measure for our collected and standard data sets.
Classification AVC for Collected Data AVC for Standard Data KStar 0 0 J84 0 0 SMO 0 0.02 Bayes Net 0 0.02 MLP 0.33 0.75 TABLE FOR PERFORMANCE MEASURE OF EACH CLASSIFIERS
ROC curves for data mining techniques using standard data set. ROC curves for data mining techniques using collected data set.
Mohammed Abdul Khaled, Sateesh Kumar Pradhan and G.N. Dash, "A survey of data mining techniques on medical data for finding locally frequent diseases," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, pp. 149-153, August 2013. R. Chitra and V.Seenivasagam, "Review of heart disease prediction system using data mining and hybrid intelligent“, ICTACT Journal on Soft Computing, vol. 03, no. 04, July 2013. Rupali, R.Patil, "Heart disease prediction system using Naive Bayes and Jelinek-mercer smothing“, International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 5, May 2014. Shamsher Bahadur Patel, Pramod Kumar Yadav and Dr. D. P. Shukla, "Predict the diagnosis of heart disease patients using classification mining Techniques“, IOSR Journal of Agriculture and Veterinary Science (IOSR- JAVS), vol. 4, no. 2, pp. 61-64, Jul.-Aug. 2013.
SUMMERY DIFFICULTIES
Micheline Kamber and Jian Pei Jiawei Han, Data Mining Concepts and Techniques, 3rd ed., 2012. "Heart Disease: Definition, Causes, Research," Available online: http://www.medicalnewstoday.com/ articles/237191.php [Accessed: 29- Jan-2016]. Abdur Razzak, "A questionnaire survey on infectious disease International Journal of Genetics and Molecular Biology, vol. 3, no. 9, pp. 120-xxx, October 2011. "Heart disease’s now a plague in Bangladesh," Available online: http://www.thedailystar.netlheart- diseases-now-a-plague-in-bangladesh- 57098. Abhishek Taneja, "Heart disease prediction system using data mining techniques," Oriental Journal of Computer Science & Technology, vol. 6, pp. 457-466, December 2013. John G. Cleary and Leonard E. Trigg, "K*: An Instance-based learner using an entropic distance measure," Proc. 12th International Conference on Machine Learning, pp. 108- 114, 1995.
Promad Kumar Yadav, K. L. Jaiswal, Shamsher Bahadur Patel, D. P. Shukla, "Intelligent heart disease prediction model using classification algorithms," UCSMC, vol. 3, no. 08, pp. 102-107, August 2013. Gaurav Taneja and Ashwini Sethi, "Comparison of classifiers in data mining," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 102-115, November 2014.. "Machine Learning Repository," Available: repository.seasr.orgjDatasets/UCI/csv R. Vijaya Kumar Reddy, K. Prudvi Raju, M. Jogendra Kumar, CH. Sujatha, P. Ravi Prakash, "Prediction ofheart disease using decision tree approach," International Journal of Advanced Research in Computer Science and Engineering, vol. 6, no. 3, March 2016. M. Hall, E. Frank, G.Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, "The weka data mining software: An update," SIGKDD explorations, vol. 11, 2009. Hlaudi Daniel Masethe and Mosima Anna Masethe, in Proceeding of the World Congress on Engineering and Computer Science, vol. II, San Francisco, USA, 2014.
Heart Disease Data Mining Using Classification Techniques

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Heart Disease Data Mining Using Classification Techniques

  • 1.
  • 3. PAPER REVIEW HEART DISEASE DATA MINING
  • 5. BEGIN DATASETS 10-FOLD CROSS VALIDATION SELECTION CLASSIFIER SELECTION KSTAR J84 SMO BAYES NET MLP PERFORMENCE ANALYSIS OF THE SELECTED CLASSIFIER END
  • 6. TECHNIQUES MLP (Multi-layer Perceptron) Decision Tree Bayes Net KStar SMO (Sequential Minimal Optimization)
  • 8. Classification Collected Data Standard Data Accuracy Model Building Time (s) Accuracy Model Building Time (s) KStar 75% 0 75.1852% 0 J84 86% 0 76.6667% 0 SMO 89% 0 84.0741% 0.02 Bayes Net 87% 0 81.1111% 0.02 MLP 86% 0.33 77.4074% 0.75 TABLE FOR PERFORMANCE MEASURE OF CLASSIFIERS
  • 9. Comparative study of accuracy measure for our collected and standard data sets.
  • 10. Classification AVC for Collected Data AVC for Standard Data KStar 0 0 J84 0 0 SMO 0 0.02 Bayes Net 0 0.02 MLP 0.33 0.75 TABLE FOR PERFORMANCE MEASURE OF EACH CLASSIFIERS
  • 11. ROC curves for data mining techniques using standard data set. ROC curves for data mining techniques using collected data set.
  • 12. Mohammed Abdul Khaled, Sateesh Kumar Pradhan and G.N. Dash, "A survey of data mining techniques on medical data for finding locally frequent diseases," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, pp. 149-153, August 2013. R. Chitra and V.Seenivasagam, "Review of heart disease prediction system using data mining and hybrid intelligent“, ICTACT Journal on Soft Computing, vol. 03, no. 04, July 2013. Rupali, R.Patil, "Heart disease prediction system using Naive Bayes and Jelinek-mercer smothing“, International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 5, May 2014. Shamsher Bahadur Patel, Pramod Kumar Yadav and Dr. D. P. Shukla, "Predict the diagnosis of heart disease patients using classification mining Techniques“, IOSR Journal of Agriculture and Veterinary Science (IOSR- JAVS), vol. 4, no. 2, pp. 61-64, Jul.-Aug. 2013.
  • 14. Micheline Kamber and Jian Pei Jiawei Han, Data Mining Concepts and Techniques, 3rd ed., 2012. "Heart Disease: Definition, Causes, Research," Available online: http://www.medicalnewstoday.com/ articles/237191.php [Accessed: 29- Jan-2016]. Abdur Razzak, "A questionnaire survey on infectious disease International Journal of Genetics and Molecular Biology, vol. 3, no. 9, pp. 120-xxx, October 2011. "Heart disease’s now a plague in Bangladesh," Available online: http://www.thedailystar.netlheart- diseases-now-a-plague-in-bangladesh- 57098. Abhishek Taneja, "Heart disease prediction system using data mining techniques," Oriental Journal of Computer Science & Technology, vol. 6, pp. 457-466, December 2013. John G. Cleary and Leonard E. Trigg, "K*: An Instance-based learner using an entropic distance measure," Proc. 12th International Conference on Machine Learning, pp. 108- 114, 1995.
  • 15. Promad Kumar Yadav, K. L. Jaiswal, Shamsher Bahadur Patel, D. P. Shukla, "Intelligent heart disease prediction model using classification algorithms," UCSMC, vol. 3, no. 08, pp. 102-107, August 2013. Gaurav Taneja and Ashwini Sethi, "Comparison of classifiers in data mining," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 102-115, November 2014.. "Machine Learning Repository," Available: repository.seasr.orgjDatasets/UCI/csv R. Vijaya Kumar Reddy, K. Prudvi Raju, M. Jogendra Kumar, CH. Sujatha, P. Ravi Prakash, "Prediction ofheart disease using decision tree approach," International Journal of Advanced Research in Computer Science and Engineering, vol. 6, no. 3, March 2016. M. Hall, E. Frank, G.Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, "The weka data mining software: An update," SIGKDD explorations, vol. 11, 2009. Hlaudi Daniel Masethe and Mosima Anna Masethe, in Proceeding of the World Congress on Engineering and Computer Science, vol. II, San Francisco, USA, 2014.