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Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm

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Abstract

Heart disease is the most life-threatening disease globally, affecting human life very critically. On-time and precise diagnosis of heart disease is vital for the prevention and treatment of heart failure. In the earlier stage, several detection techniques were developed to detect heart disease using machine learning techniques. But the heart problems have not been detected accurately with the minimum required time. This paper presented an effective heart disease prediction system that accurately diagnoses abnormality within a short duration. The feature selection process is carried with stochastic gradient boosting with a recursive feature elimination approach. For classification, the features are clustered by using the adaptive Harris hawk optimization clustering approach. Based on clustered features, the classification is processed with an enhanced deep genetic algorithm. This method improves the performance of the deep neural network by augmenting its initial weights with an enhanced genetic algorithm, which recommends better weights for the neural network. The demonstration of the proposed work is simulated in the MATLAB platform and the dataset used in UCI machine learning repository. The experimental outcome shows that the developed feature selection and classification approach attain 98.36% and 97.3% accuracy compared with the state-of-the-art techniques.

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Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Authors and Affiliations

  1. Department of Mechanical Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, Karur, Tamil Nadu, 639113, India

    R. Balamurugan

  2. Sea Sense Softwares Pvt Ltd. Marthandam, KanyaKumari, India

    S. Ratheesh & Y. Maria Venila

Authors
  1. R. Balamurugan
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  2. S. Ratheesh
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  3. Y. Maria Venila
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Correspondence to R. Balamurugan.

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R. Balamurugan, Dr. S. Ratheesh and Y. Maria Venila declared that they have no conflict of interest.

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Balamurugan, R., Ratheesh, S. & Venila, Y.M. Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm. Soft Comput 26, 2357–2373 (2022). https://doi.org/10.1007/s00500-021-06536-0

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