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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
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    Cited by:

    1. 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.
    2. 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.

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