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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
www.ijarcs.info Internet Source.
Shirazi, Syed Noorulhassan, Antonios Gouglidis, Kanza Noor Syeda, Steven Simpson et al. Evaluation of Anomaly Detection Techniques for SCADA Communication Resilience. Resilience Week (RWS).
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.
Goyal, Anshul, Rajni Mehta. 2012. Performance Comparison of Naïve Bayes and J48 Classification Algorithms. IJAER 7 (11).
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).
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.
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.
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).
Communications in Computer and Information Science, 2016.
Vijayarani, S. 2013. Evaluating the Efficiency of Rule Techniques for File Classification. International Journal of Research in Engineering and Technology.
Submitted to The University of the South Pacific Student Paper.
idus.us.es. Internet Source.
http://journal.frontiersin.org. Internet Source.
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).
research.ijcaonline.org Internet Source.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Managathayaru, N. et al. (2020). Diagnosis of Diabetes Using Clinical Decision Support System. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_29
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)