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Computational Intelligence Approaches for Heart Disease Detection

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Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 855))

Abstract

A disease is an occurrence that affects one or more areas of a person's body. Various diseases are on the rise as a result of changing lifestyles and patrimonial values. Heart disease (HD) is the most serious of all disorders, and its consequences are much more dangerous than those of any other disease. Therefore, early detection of HD will reduce the death rate of people. Computational Intelligence (CI) approaches can be employed for the early diagnosis and detection of HD. Hence, employed the use of two computational intelligence approaches. The study compared a variety of computational intelligence strategies for heart disease detection. A comparison analysis was drawn using Two computational intelligence techniques: Decision Tree (DT) and K-Nearest Neighbor (KNN). A feature extraction algorithm which is Autoencoder was employed to reduce the number of attributes required to describe the heart disease dataset. The performance of each approach was measured using heart disease databases obtained from the National Health Service (NHS) database and uncertainty matrix success assessment metrics.

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Acknowledgments

The authors appreciate the sponsorship from Covenant University through its Centre for Research, Innovation and Discovery, Covenant University, Ota Nigeria.

Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Roseline Oluwaseun Ogundokun

  2. Center of ICT/ICE Research, Covenant University, Ota, Ogun State, Nigeria

    Sanjay Misra

  3. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Peter Ogirima Sadiku

  4. Birla Institute of Technology Pilani, Pilani, India

    Himanshu Gupta

  5. Kaunas University of Technology, Kaumas, Lithuania

    Robertas Damasevicius & Rytis Maskeliunas

Authors
  1. Roseline Oluwaseun Ogundokun
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  2. Sanjay Misra
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  3. Peter Ogirima Sadiku
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  4. Himanshu Gupta
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  5. Robertas Damasevicius
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  6. Rytis Maskeliunas
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Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

Editor information

Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Department of CSE, Central University of Jammu, Jammu and Kashmir, India

    Yashwant Singh

  3. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Jitender Kumar Chhabra

  4. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Zoltán Illés

  5. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Chaman Verma

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Ogundokun, R.O., Misra, S., Sadiku, P.O., Gupta, H., Damasevicius, R., Maskeliunas, R. (2022). Computational Intelligence Approaches for Heart Disease Detection. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_29

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