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BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting

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Abstract

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

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Notes

  1. <http://archive.ics.uci.edu/ml/datasets.html> [last Accessed: Sep 25 2013].

  2. <http://archive.ics.uci.edu/ml/datasets.html> [last Accessed: Sep 25 2013].

  3. http://en.wikipedia.org/wiki/Rawalpindi_Institute_of_Cardiology [Last accessed on 8th December, 2014].

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Acknowledgments

We are grateful to Rawalpindi Institute of Cardiology for their support in using the proposed DSS for research purposes only under the strict supervision of a team of medical experts and their information technology team.

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Authors and Affiliations

  1. Computer Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Saba Bashir, Usman Qamar & Farhan Hassan Khan

Authors
  1. Saba Bashir
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  2. Usman Qamar
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  3. Farhan Hassan Khan
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Corresponding author

Correspondence to Farhan Hassan Khan.

Appendix

Appendix

See Table 14.

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Bashir, S., Qamar, U. & Khan, F.H. BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas Phys Eng Sci Med 38, 305–323 (2015). https://doi.org/10.1007/s13246-015-0337-6

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  • DOI: https://doi.org/10.1007/s13246-015-0337-6

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