Comparative analysis of prediction algorithms for diabetes

Shweta Karun, Aishwarya Raj, Girija Attigeri

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

Abstract

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.

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
Pages177-187
Number of pages11
ISBN (Print)9789811303401
DOIs
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
Volume759
ISSN (Print)2194-5357

Conference

ConferenceInternational Conference on Computer, Communication and Computational Sciences, IC4S 2017
CountryThailand
CityKathu
Period11-10-1712-10-17

Fingerprint

Medical problems
Feature extraction
Learning systems
Decision trees
Learning algorithms
Support vector machines
Logistics
Neural networks

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Karun, S., Raj, A., & Attigeri, G. (2019). Comparative analysis of prediction algorithms for diabetes. In S. K. Bhatia, S. Tiwari, M. C. Trivedi, & K. K. Mishra (Eds.), Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017 (pp. 177-187). (Advances in Intelligent Systems and Computing; Vol. 759). Springer Verlag. https://doi.org/10.1007/978-981-13-0341-8_16
Karun, Shweta ; Raj, Aishwarya ; Attigeri, Girija. / Comparative analysis of prediction algorithms for diabetes. Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017. editor / Sanjiv K. Bhatia ; Shailesh Tiwari ; Munesh C. Trivedi ; Krishn K. Mishra. Springer Verlag, 2019. pp. 177-187 (Advances in Intelligent Systems and Computing).
@inproceedings{95e5bba614de4608be2e26d8e22198c6,
title = "Comparative analysis of prediction algorithms for diabetes",
abstract = "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, na{\"i}ve 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.",
author = "Shweta Karun and Aishwarya Raj and Girija Attigeri",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-981-13-0341-8_16",
language = "English",
isbn = "9789811303401",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "177--187",
editor = "Bhatia, {Sanjiv K.} and Shailesh Tiwari and Trivedi, {Munesh C.} and Mishra, {Krishn K.}",
booktitle = "Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017",
address = "Germany",

}

Karun, S, Raj, A & Attigeri, G 2019, Comparative analysis of prediction algorithms for diabetes. in SK Bhatia, S Tiwari, MC Trivedi & KK Mishra (eds), Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017. Advances in Intelligent Systems and Computing, vol. 759, Springer Verlag, pp. 177-187, International Conference on Computer, Communication and Computational Sciences, IC4S 2017, Kathu, Thailand, 11-10-17. https://doi.org/10.1007/978-981-13-0341-8_16

Comparative analysis of prediction algorithms for diabetes. / Karun, Shweta; Raj, Aishwarya; Attigeri, Girija.

Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017. ed. / Sanjiv K. Bhatia; Shailesh Tiwari; Munesh C. Trivedi; Krishn K. Mishra. Springer Verlag, 2019. p. 177-187 (Advances in Intelligent Systems and Computing; Vol. 759).

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

TY - GEN

T1 - Comparative analysis of prediction algorithms for diabetes

AU - Karun, Shweta

AU - Raj, Aishwarya

AU - Attigeri, Girija

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85053249043&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053249043&partnerID=8YFLogxK

U2 - 10.1007/978-981-13-0341-8_16

DO - 10.1007/978-981-13-0341-8_16

M3 - Conference contribution

SN - 9789811303401

T3 - Advances in Intelligent Systems and Computing

SP - 177

EP - 187

BT - Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017

A2 - Bhatia, Sanjiv K.

A2 - Tiwari, Shailesh

A2 - Trivedi, Munesh C.

A2 - Mishra, Krishn K.

PB - Springer Verlag

ER -

Karun S, Raj A, Attigeri G. Comparative analysis of prediction algorithms for diabetes. In Bhatia SK, Tiwari S, Trivedi MC, Mishra KK, editors, Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017. Springer Verlag. 2019. p. 177-187. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-13-0341-8_16