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Relevant subset computation using mutually dependent features and normalized divergence isolation forest using bio-image of heart to classify coronary heart disease

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Abstract

Machine learning methodologies have gained attention in classifying clinical problems in recent years. Coronary heart disease is a significant clinical problem to be addressed and classified using machine learning strategies. The outliers should be handled better to improve the model’s overall performance. Our research work proposed two different methods to avoid outliers from the data set. Feature selection from the dataset was made through the mutual information-based feature selection to compute the relevant set of features from the Bio image of heart through electrocardiographic signals. The outlier in the data has been reduced and classification is done using proposed isolation forest. Normalization is adopted to minimize misclassification, which has improved the overall accuracy, specificity, and sensitivity of the proposed machine learning model from the existing algorithm. A benchmark coronary dataset that has 303 data samples and 76 features had been utilized for research purposes.

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References

  • Balajee, A., Venkatesan, R.: Machine learning based identification and classification of disorders in human knee joint–computational approach. Soft. Comput. 25(20), 13001–13013 (2021)

    Article  Google Scholar 

  • Chauhan, S., Aeri, B.T.: The rising incidence of cardiovascular diseases in India: assessing its economic impact. J Prev. Cardiol. 4(4), 735–740 (2015)

    Google Scholar 

  • Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2, 265–292 (2001)

    Google Scholar 

  • Fida, B., Nazir, M., Naveed, N., Akram, S.: Heart disease classification ensemble optimization using genetic algorithm. In: 2011 IEEE 14th International Multitopic Conference, pp. 19–24. IEEE (2011)

  • Gupta, N., Ahuja, N., Malhotra, S., Bala, A., Kaur, G.: Intelligent heart disease prediction in cloud environment through ensembling. Expert. Syst. 34(3), 356–367 (2017)

    Article  Google Scholar 

  • Khazaee, A.: Heart beat classification using particle swarm optimization. Int. J. Intell. Syst. Appl. 5(6), 25–33 (2013)

    Google Scholar 

  • Lee, H.G., Noh, K.Y., Ryu, K.H.: Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 218–228. Springer, Berlin, Heidelberg (2007)

  • Mallikarjuna Reddy, A., Reddy, K.S., Jayaram, M., VenkataMaha Lakshmi, N., Aluvalu, R., Mahesh, T.R., Vinoth Kumar, V., Stalin Alex, D.: An efficient multilevel thresholding scheme for heart image segmentation using a hybrid generalized adversarial network. J. Sens. 2022, 1–11 (2022). https://doi.org/10.1155/2022/4093658

    Article  Google Scholar 

  • Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 19(7), 81542–81554 (2019)

    Article  Google Scholar 

  • Moon, J.R., Huh, J., Song, J., Kang, I.S., Park, S.W., Chang, S.A., Yang, J.H., Jun, T.G., Han, J.S.: The effects of rational emotive behavior therapy for depressive symptoms in adults with congenital heart disease. Heart Lung 50(6), 906–913 (2021)

    Article  PubMed  Google Scholar 

  • Nahar, J., Imam, T., Tickle, K.S., Chen, Y.P.: Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst. Appl. 40(1), 96–104 (2013)

    Article  Google Scholar 

  • Pincus, S.: Approximate entropy (ApEn) as a complexity measure. Chaos: Interdiscip. J. Nonlinear Sci. 5(1), 110–117 (1995)

    Article  MathSciNet  Google Scholar 

  • Pincus, S.M., Viscarello, R.R.: Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet. Gynecol. 79(2), 249–255 (1992)

    CAS  PubMed  Google Scholar 

  • Rajendran, R., Karthi, A.: Heart disease prediction using entropy based feature engineering and ensembling of machine learning classifiers. Expert Syst. Appl. 30(207), 1–15 (2022)

    Article  Google Scholar 

  • Ramakrishna, M.T., Venkatesan, V.K., Izonin, I., Havryliuk, M., Bhat, C.R.: Homogeneous adaboost ensemble machine learning algorithms with reduced entropy on balanced data. Entropy 25, 245–259 (2023). https://doi.org/10.3390/e25020245

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Saini, I., Singh, D., Khosla, A.: Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine. Comput. Electr. Eng. 40(5), 1774–1787 (2014)

    Article  Google Scholar 

  • Salcedo-Bernal, A., Villamil-Giraldo, M.P., Moreno-Barbosa, A.D.: Clinical data analysis: an opportunity to compare machine learning methods. Proc. Comput. Sci. 1(100), 731–738 (2016)

    Article  Google Scholar 

  • Santhakumar, D., Logeswari, S.: Hybrid ant lion mutated ant colony optimizer technique for Leukemia prediction using microarray gene data. J. Ambient. Intell. Humaniz. Comput. 12(2), 2965–2973 (2021)

    Article  Google Scholar 

  • Sharada, K.A., Sushma, K.S.N., Muthukumaran, V., Mahesh, T.R., Swapna, B., Roopashree, S.: High ECG diagnosis rate using novel machine learning techniques with distributed arithmetic (DA) based gated recurrent units. Microprocess. Microsyst. 98, 1–7 (2023). https://doi.org/10.1016/j.micpro.2023.104796

    Article  Google Scholar 

  • Song, Y., Crowcroft, J., Zhang, J.: Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J. Neurosci. Methods 210(2), 132–146 (2012)

    Article  PubMed  Google Scholar 

  • Taloba, A.I., Alanazi, R., Shahin, O.R., Elhadad, A.: Machine algorithm for heartbeat monitoring and arrhythmia detection based on ECG systems. Comput. Intell. Neurosci. 2021, 1–9 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  • Thilagamani, S.: A survey on efficient heart disease prediction technique. Turk. J. Comput. Math. Educ. (TURCOMAT) 12(9), 130–136 (2021)

    Google Scholar 

  • Thomas, J., Princy, R.T.: Human heart disease prediction system using data mining techniques. In: 2016 International Conference On Circuit, Power And Computing Technologies (ICCPCT), pp. 1–5. IEEE (2016)

  • Ubeyli, E.D.: ECG beats classification using multiclass support vector machines with error correcting output codes. Digit. Signal Process. 17(3), 675–684 (2007)

    Article  Google Scholar 

  • Venkatesan, V.K., Ramakrishna, M.T., Izonin, I., Tkachenko, R., Havryliuk, M.: Efficient data preprocessing with ensemble machine learning technique for the early detection of chronic kidney disease. Appl. Sci. 13, 1–18 (2023)

    Article  CAS  Google Scholar 

  • Xing, Y., Wang, J., Zhao, Z.: Combination data mining methods with new medical data to predicting outcome of coronary heart disease. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007), pp. 868–872. IEEE (2007)

  • Zhang, R., Hao, Y., Wang, Y., Yang, H.: Significant association between ischemic heart disease and elevated risk for COVID-19 mortality: a meta-analysis. Am. J. Emerg. Med. 55, 95–97 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

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  1. Department of Computer Science and Engineering, Puducherry Technological University, Puducherry, India

    K. Pragash & J. Jayabharathy

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  1. K. Pragash
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  2. J. Jayabharathy
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KP—wrote the main manuscript tex

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Pragash, K., Jayabharathy, J. Relevant subset computation using mutually dependent features and normalized divergence isolation forest using bio-image of heart to classify coronary heart disease. Opt Quant Electron 56, 489 (2024). https://doi.org/10.1007/s11082-023-06144-2

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