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Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

Author

Listed:
  • Abhinav Juneja

    (KIET Group of Institutions, Delhi NCR, Ghaziabad, India)

  • Sapna Juneja

    (IITM Group of Institutions, Murthal, India)

  • Sehajpreet Kaur

    (B.M. Institute of Engineering and Technology, Sonepat, India)

  • Vivek Kumar

    (B.M. Institute of Engineering and Technology, Sonepat, India)

Abstract

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

Suggested Citation

  • Abhinav Juneja & Sapna Juneja & Sehajpreet Kaur & Vivek Kumar, 2021. "Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 11(2), pages 38-52, April.
    • Handle: RePEc:igg:jirr00:v:11:y:2021:i:2:p:38-52
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    Cited by:

    1. Gaurav Dhiman & Sapna Juneja & Hamidreza Mohafez & Ibrahim El-Bayoumy & Lokesh Kumar Sharma & Maryam Hadizadeh & Mohammad Aminul Islam & Wattana Viriyasitavat & Mayeen Uddin Khandaker, 2022. "Federated Learning Approach to Protect Healthcare Data over Big Data Scenario," Sustainability, MDPI, vol. 14(5), pages 1-14, February.

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