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Impact of Ensemble Learning Algorithms Towards Accurate Heart Disease Prediction


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1 Department of Computer Science, K.R. College of Arts and Science, India
     

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The medical field comprises of various techniques. Yet, the Data mining is playing a crucial role in determining the future of medications and patients’ state. This is because of the reliability offered by the various classification techniques. Still, accurate prediction of heart disease is becoming more and more challenging due to the influence of the various factors extracted from patients. Identifying these factors is a crucial research task. In such a scenario, the individual classification algorithms fail to produce perfect models capable of accurately predicting the heart disease. Hence, by introducing the ensemble learning methods, higher performance could be achieved leading to the accurate prediction of heart diseases. In this research work, the performance of the three ensemble classifiers namely Bagging, Stacking and AdaBoost is experimented and evaluated on various folds of cross validation with benchmark dataset for heart disease prediction. The base learners considered for constructing the ensemble are well known classifiers namely Support Vector Machine, Naive Bayes and K-Nearest Neighbour. The results illustrate the improved performance in terms of performance metrics and provide a better understanding of the accuracy, reliability and usefulness of the ensemble models in favouring improved performance for heart disease prediction.

Keywords

Heart Disease, Patient Health, Prediction, Classification, Ensemble Classifier, Data Mining.
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  • L. Breiman, “Bagging Predictors”, Machine Learning, Vol. 24, No. 2, pp. 123-140, 1996.
  • R. Li, S. Shen, G. Chen, T. Xie, S. Ji, B. Zhou and Z. Wang, “Multilevel Risk Prediction of Cardiovascular Disease based on Adaboost+RF Ensemble Learning”, IOP Publisher, 2019.
  • Eibe Frank, Mark Hall, Len Trigg, Geoffrey Holmes and Ian H. Witten, “Data Mining in Bioinformatics using Weka”, Bioinformatics, Vol. 20, No. 15, pp. 2479-2481, 2004.
  • Franck Le Duff, Cristian Muntean, Marc Cuggia and Philippe Mabo, “Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method”, Studies in Health Technology and Informatics, Vol. 107, No. 2, pp. 1256-1259, 2004.
  • Nan-Chen and Lun-Ping Hung, “A Data Driven Ensemble Classifier for Credit Scoring Analysis”, Expert systems with Applications, Vol. 37, No. 1, pp. 534-545, 2010.
  • Latha Parthiban and R. Subramanian, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm”, International Journal of Biological, Biomedical and Medical Sciences, Vol. 3, No. 3, pp. 1-8, 2008.
  • Nidhi Singh and Divakar Singh, “Performance Evaluation of K-Means and Hierarchal Clustering in Terms of Accuracy and Running Time”, Ph.D. Dissertation, Department of Computer Science and Engineering, Barkatullah University Institute of Technology, 2012.
  • Sellappan Palaniappan and Rafiah Awang, “Intelligent Heart Disease Prediction System using Data Mining Techniques”, International Journal of Computer Science and Network Security, Vol. 8, No. 8, pp. 1-6, 2008.
  • W.J. Frawley and G. Piatetsky-Shapiro, “Knowledge Discovery in Databases: An Overview”, AI Magazine, Vol. 13, No. 3, pp. 57-70, 1996.
  • X. Yanwei, J. Wang, Z. Zhao and Y. Gao, “Combination Data Mining Models with New Medical Data to Predict Outcome of Coronary Heart Disease”, Proceedings of International Conference on Convergence Information Technology, pp. 868-872, 2007.
  • K.H. Miao, J.H., Miao and G.J. Miao, “Diagnosing Coronary Heart Disease using Ensemble Machine Learning”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, pp. 1-12, 2016.
  • R. Das and A, Sengur, “Evaluation of Ensemble Methods for Diagnosing of Valvular Heart Disease”, Expert Systems with Applications, Vol. 37, No. 7, pp. 5110-5115, 2010.
  • I. Yekkala, S. Dixit and M.A. Jabbar, “Prediction of Heart Disease using Ensemble Learning and Particle Swarm Optimization”, Proceedings of International Conference on Smart Technologies for Smart Nation, pp. 691-698, 2017.
  • S. Ekiz and P. Erdogmuş, “Comparative Study of Heart Disease Classification”, Proceedings of International Conference on Electric Electronics, Computer Science, Biomedical Engineering, pp. 1-4, 2017.
  • K. Raza, “Improving the Prediction Accuracy of Heart Disease with Ensemble Learning and Majority Voting Rule”, Academic Press, 2019.
  • H. Benjamin Fredrick David and S. Antony Belcy, “Heart Disease Prediction using Data Mining Techniques”, ICTACT Journal on Soft Computing, Vol. 9, No. 1, pp. 1817-1823, 2018.
  • H.B.F. David, R. Balasubramanian and A.A. Pandian, “CBIR Using Multi-Resolution Transform for Brain Tumour Detection and Stages Identification”, International Journal of Biomedical and Biological Engineering, Vol. 10, No. 11, pp. 543-553, 2016.
  • H.B.F. David and A. Suruliandi, “Empirical Study of Ensemble Classifications on Benchmark Datasets”, Journal of Analysis and Computing, Vol. 12, No. 2, pp. 1-14, 2018.

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  • Impact of Ensemble Learning Algorithms Towards Accurate Heart Disease Prediction

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Authors

H. Benjamin Fredrick David
Department of Computer Science, K.R. College of Arts and Science, India

Abstract


The medical field comprises of various techniques. Yet, the Data mining is playing a crucial role in determining the future of medications and patients’ state. This is because of the reliability offered by the various classification techniques. Still, accurate prediction of heart disease is becoming more and more challenging due to the influence of the various factors extracted from patients. Identifying these factors is a crucial research task. In such a scenario, the individual classification algorithms fail to produce perfect models capable of accurately predicting the heart disease. Hence, by introducing the ensemble learning methods, higher performance could be achieved leading to the accurate prediction of heart diseases. In this research work, the performance of the three ensemble classifiers namely Bagging, Stacking and AdaBoost is experimented and evaluated on various folds of cross validation with benchmark dataset for heart disease prediction. The base learners considered for constructing the ensemble are well known classifiers namely Support Vector Machine, Naive Bayes and K-Nearest Neighbour. The results illustrate the improved performance in terms of performance metrics and provide a better understanding of the accuracy, reliability and usefulness of the ensemble models in favouring improved performance for heart disease prediction.

Keywords


Heart Disease, Patient Health, Prediction, Classification, Ensemble Classifier, Data Mining.

References