IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v13y2018i4p82-97.html
   My bibliography  Save this article

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

Author

Listed:
  • P Priyanga

    (Dept. of CSE, K.S. Institute of Technology, Bengaluru, India)

  • N C. Naveen

    (Dept. of CSE, J S S Academy of Technical Education, Bengaluru, India)

Abstract

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

Suggested Citation

  • P Priyanga & N C. Naveen, 2018. "Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 13(4), pages 82-97, October.
    • Handle: RePEc:igg:jhisi0:v:13:y:2018:i:4:p:82-97
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2018100106
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Priti Bansal & Sumit Kumar & Ritesh Srivastava & Saksham Agarwal, 2021. "Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(2), pages 73-86, April.
    2. Aritra Pan & Shameek Mukhopadhyay & Subrata Samanta, 2022. "RETRACTED: Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(2), pages 1-19, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:13:y:2018:i:4:p:82-97. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.