Advertisement

Diagnosis of Diabetes Using Clinical Decision Support System

  • N. ManagathayaruEmail author
  • B. Mathura Bai
  • G. Sunil
  • G. Hanisha Durga
  • C. Anjani Varma
  • V. Sai Sarath
  • J. Sai Sandeep
Conference paper
  • 10 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)

Abstract

Medicinal services are one of the prime worries of each individual. This work deals with diabetes, an incessant illness which is exceptionally regular throughout the world. Administration of such complex ailments requires proper diagnosis for which efficient analysis is required. So, extracting the diabetes reports in productive way is an essential concern. The Pima Indian Diabetes Data Set is used for this project, which accumulates the data of individuals who are affected and not affected by diabetes. The work goes for discovering solutions to analyze the illness by looking at patterns found in the information through classification analysis. The altered J48 classifier is applied to enhance the precision rate before which preprocessing and feature selection have been done as this prompts to decisions which are more accurate. The research would like to promote an agile and more proficient method of diagnosing the malady, prompting better treatment of the patients.

Keywords

Clinical decision support system J48 decision tree Diabetes Missing values Normalization Feature selection 

Notes

Acknowledgements

The proposed research work has been funded under DRDO-LSRB (DRDO-Life Science Research Board)—No. CC R&D (TM)/81/48222/LSRB-284.

References

  1. 1.
    www.ijarcs.info Internet Source.
  2. 2.
    Shirazi, Syed Noorulhassan, Antonios Gouglidis, Kanza Noor Syeda, Steven Simpson et al. Evaluation of Anomaly Detection Techniques for SCADA Communication Resilience. Resilience Week (RWS).Google Scholar
  3. 3.
    Kumari, S., and A. Singh. 2013. A data mining approach for the diagnosis of Diabetes Mellitus. In Proceedings of Seventh International Conference on Intelligent Systems and Control, pp. 373–375.Google Scholar
  4. 4.
    Goyal, Anshul, Rajni Mehta. 2012. Performance Comparison of Naïve Bayes and J48 Classification Algorithms. IJAER 7 (11).Google Scholar
  5. 5.
    Magudeeswaran, G., D. Suganyadevi. 2013. Forecast of Diabetes using Modified Radial basis Functional Neural Networks. In International Conference on Research Trends in Computer Technologies (ICRTCT). Proceedings Published in International Journal of Computer Applications (IJCA) (0975-8887).Google Scholar
  6. 6.
    Karegowda, A.G., M.A. Jayaram, A.S. Manjunath. 2012. Cascading K-means Clustering and K-Nearest Neighbor Classifier for Categorization of Diabetic Patients. International Journal of Engineering and Advanced Technology (IJEAT) 1 (1). ISSN: 2249 – 8958.Google Scholar
  7. 7.
    Mathura Bai, B., N. Mangathayaru, and B. Padmaja Rani. 2015. An Approach to Find Missing Values in Medical Datasets. In Proceedings of the International Conference on Engineering & MIS.Google Scholar
  8. 8.
    Jahangir, Maham , Hammad Afzal, Mehreen Ahmed, Khawar Khurshid, and Raheel Nawaz. 2017. An Expert System for Diabetes Prediction Using Auto Tuned Multi-layer Perceptron. In Intelligent Systems Conference (IntelliSys).Google Scholar
  9. 9.
    Communications in Computer and Information Science, 2016.Google Scholar
  10. 10.
    Vijayarani, S. 2013. Evaluating the Efficiency of Rule Techniques for File Classification. International Journal of Research in Engineering and Technology.Google Scholar
  11. 11.
    Submitted to The University of the South Pacific Student Paper.Google Scholar
  12. 12.
    idus.us.es. Internet Source.Google Scholar
  13. 13.
  14. 14.
    Saravanan, N., V. Gayathri. 2017. Classification of Dengue Dataset Using J48 Algorithm and Ant Colony Based AJ48 Algorithm. In International Conference on Inventive Computing and Informatics (ICICI).Google Scholar
  15. 15.
    research.ijcaonline.org Internet Source.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • N. Managathayaru
    • 1
    Email author
  • B. Mathura Bai
    • 1
  • G. Sunil
    • 1
  • G. Hanisha Durga
    • 1
  • C. Anjani Varma
    • 1
  • V. Sai Sarath
    • 1
  • J. Sai Sandeep
    • 1
  1. 1.Department of Information TechnologyVNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia

Personalised recommendations