Comparative analysis of prediction algorithms for diabetes

Shweta Karun, Aishwarya Raj, Girija Attigeri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Machine learning is a widely growing field which helps in better learning from data and its analysis without any human intervention. It is being popularly used in the field of healthcare for analyzing and detecting serious and complex conditions. Diabetes is one such condition that heavily affects the entire system. In this paper, application of intelligent machine learning algorithms like logistic regression, naïve Bayes, support vector machine, decision tree, k-nearest neighbors, neural network, and random decision forest are used along with feature extraction. The accuracy of each algorithm, with and without feature extraction, leads to a comparative study of these predictive models. Therefore, a list of algorithms that works better with feature extraction and another that works better without it is obtained. These results can be used further for better prediction and diagnosis of diabetes.

Original languageEnglish
Title of host publicationAdvances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017
EditorsSanjiv K. Bhatia, Shailesh Tiwari, Munesh C. Trivedi, Krishn K. Mishra
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9789811303401
Publication statusPublished - 01-01-2019
EventInternational Conference on Computer, Communication and Computational Sciences, IC4S 2017 - Kathu, Thailand
Duration: 11-10-201712-10-2017

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


ConferenceInternational Conference on Computer, Communication and Computational Sciences, IC4S 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)


Dive into the research topics of 'Comparative analysis of prediction algorithms for diabetes'. Together they form a unique fingerprint.

Cite this