Abstract
Heart disease prediction is a critical task regarding human health. It is based on deriving an Machine Learning model from medical parameters to predict risk levels. In this work, we propose and test novel ensemble methods for heart disease prediction. Randomness analysis of distance sequences is utilized to derive a classifier, which is served as a base estimator of a bagging scheme. Method is successfully tested on medical Spectf dataset. Additionally, a Graph Lasso and Ledoit–Wolf shrinkage-based classifier is developed for Statlog dataset which is a UCI data. These two algorithms yield comparatively good accuracy results: 88.7 and 88.8 for Spectf and Statlog, respectively. These proposed algorithms provide promising results and novel classification methods that can be utilized in various domains to improve performance of ensemble methods.
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Agarwal, S., Tomar, D.: A feature selection based model for software defect prediction. Assessment 65, (2014)
Alcover, P.M., Guillamón, A., Ruiz, M.D.C.: A new randomness test for bit sequences. Informatica 24(3), 339–356 (2013)
Bashir, S., Khan, Z.S., Khan, F.H., Anjum, A., Bashir, K.: Improving heart disease prediction using feature selection approaches. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 619–623. IEEE (2019)
Bashir, S., Qamar, U., Khan, F.H., Javed, M.Y.: Mv5: a clinical decision support framework for heart disease prediction using majority vote based classifier ensemble. Arab J Sci Eng 39(11), 7771–7783 (2014)
Bashir, S., Qamar, U., Khan, F.H.: Bagmoov: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Austral. Phys. Eng. Sci. Med. 38(2), 305–323 (2015)
Breiman, L.: Bagging predictors. Mach Learn 24(2), 123–140 (1996)
Breiman, L.: Random forests. Mach Learn 45(1), 5–32 (2001)
Brown, G., Pocock, A., Zhao, M.J., Luján, M.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res 13(1), 27–66 (2012)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2), 121–167 (1998)
Chen, A.H., Huang, S.Y., Hong, P.S., Cheng, C.H., Lin, E.J.: Hdps: Heart disease prediction system. In: 2011 computing in cardiology. IEEE, pp. 557–560 (2011)
Dietterich, T.G.: Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems. Springer, Berlin, pp. 1–15 (2000)
Durairaj, M., Revathi, V.: Prediction of heart disease using back propagation mlp algorithm. Int J Sci Technol Res 4(8), 235–239 (2015)
Fister, I., Fister, I., Jr., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol Comput 13, 34–46 (2013)
Flennerhag, S.: Introduction to python ensembles (2020). https://www.dataquest.io/blog/introduction-to-ensembles/
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat., 1189–1232 (2001)
Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2008)
Hu, X., Cercone, N.: Learning in relational databases: a rough set approach. Comput. Intell. 11(2), 323–338 (1995)
Juszczak, P., Tax, D., Duin, R.P.: Feature scaling in support vector data description. In: Proc. asci. Citeseer, pp. 95–102 (2002)
Karayılan, T., Kılıç, Ö.: Prediction of heart disease using neural network. In: 2017 International Conference on Computer Science and Engineering (UBMK). IEEE, pp. 719–723 (2017)
Lakshmanaprabu, S., Mohanty, S.N., Krishnamoorthy, S., Uthayakumar, J., Shankar, K., et al.: Online clinical decision support system using optimal deep neural networks. Appl. Soft Comput. 81, 105487 (2019)
Lakshmanaprabu, S., Mohanty, S.N., Shankar, K., Arunkumar, N., Ramirez, G.: Optimal deep learning model for classification of lung cancer on ct images. Future Gen. Comput. Syst. 92, 374–382 (2019)
Ledoit, O., Wolf, M.: A well conditioned estimator for large dimensional covariance matrices (2000)
Long, N.C., Meesad, P.: An optimal design for type-2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction. J. Intell. Fuzzy Syst. 27(3), 1335–1346 (2014)
Long, N.C., Meesad, P., Unger, H.: A highly accurate firefly based algorithm for heart disease prediction. Expert Syst. Appl. 42(21), 8221–8231 (2015)
Malav, A., Kadam, K., Kamat, P.: Prediction of heart disease using k-means and artificial neural network as hybrid approach to improve accuracy. Int. J. Eng. Technol. 9(4), 3081–3085 (2017)
Medhekar, D.S., Bote, M.P., Deshmukh, S.D.: Heart disease prediction system using naive bayes. Int. J. Enhanced Res. Sci. Technol. Eng. 2(3) (2013)
Moguerza, J.M., Muñoz, A., et al.: Support vector machines with applications. Stat. Sci. 21(3), 322–336 (2006)
Mythili, T., Mukherji, D., Padalia, N., Naidu, A.: A heart disease prediction model using svm-decision trees-logistic regression (sdl). Int. J. Comput. Appl. 68(16), (2013)
Parzen, E.: On spectral analysis with missing observations and amplitude modulation. In: Sankhyā: The Indian Journal of Statistics, Series A, pp. 383–392 (1963)
Pattekari, S.A., Parveen, A.: Prediction system for heart disease using naïve bayes. Int. J. Adv. Comput. Math. Sci. 3(3), 290–294 (2012)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data, vol. 9. Springer, Berlin (2012)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Radhimeenakshi, S.: Classification and prediction of heart disease risk using data mining techniques of support vector machine and artificial neural network. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp. 3107–3111 (2016)
Raybaut, P.: Spyder-documentation. Available online at: pythonhosted. org (2009)
Rish, I., et al.: An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3, pp. 41–46 (2001)
Rusk, N.: Deep learning. Nat. Methods 13(1), 35 (2016)
Sahu, B., Dash, S., Nandan Mohanty, S., Kumar Rout, S.: Ensemble comparative study for diagnosis of breast cancer datasets. Int. J. Eng. Technol. 7(4.15), 281–285 (2018)
Sahu, B., Mohanty, S., Rout, S.: A hybrid approach for breast cancer classification and diagnosis. EAI Endors. Trans. Scalable Inf. Syst. 6(20) (2019)
sklearn: Random forest classifier (2020). https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
Subbalakshmi, G., Ramesh, K., Rao, M.C.: Decision support in heart disease prediction system using naive bayes. Indian J. Comput. Sci. Eng. (IJCSE) 2(2), 170–176 (2011)
Swain, M., Kisan, S., Chatterjee, J.M., Supramaniam, M., Mohanty, S.N., Jhanjhi, N., Abdullah, A.: Hybridized machine learning based fractal analysis techniques for breast cancer classification
Turabieh, H.: A hybrid ann-gwo algorithm for prediction of heart disease. Am. J. Oper. Res. 6(2), 136–146 (2016)
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26(2), 275–309 (2013)
Acknowledgements
We would like to thank Yusuf “oblomov” Karacaören and Mehmet Fatih “quintall” Karadeniz for their support for conducting the experiments and development of the algorithm.
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Karadeniz, T., Tokdemir, G. & Maraş, H.H. Ensemble Methods for Heart Disease Prediction. New Gener. Comput. 39, 569–581 (2021). https://doi.org/10.1007/s00354-021-00124-4
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DOI: https://doi.org/10.1007/s00354-021-00124-4