Comparative analysis of prediction algorithms for diabetes

Shweta Karun; Aishwarya Raj; Girija Attigeri

doi:10.1007/978-981-13-0341-8_16

Comparative analysis of prediction algorithms for diabetes

Shweta Karun
, Aishwarya Raj
, Girija Attigeri^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

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.

Original language	English
Title of host publication	Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2017
Editors	Sanjiv K. Bhatia, Shailesh Tiwari, Munesh C. Trivedi, Krishn K. Mishra
Publisher	Springer Verlag
Pages	177-187
Number of pages	11
ISBN (Print)	9789811303401
DOIs	https://doi.org/10.1007/978-981-13-0341-8_16
Publication status	Published - 01-01-2019
Event	International Conference on Computer, Communication and Computational Sciences, IC4S 2017 - Kathu, Thailand Duration: 11-10-2017 → 12-10-2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	759
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Computer, Communication and Computational Sciences, IC4S 2017
Country/Territory	Thailand
City	Kathu
Period	11-10-17 → 12-10-17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

Access to Document

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

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",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; International Conference on Computer, Communication and Computational Sciences, IC4S 2017 ; Conference date: 11-10-2017 Through 12-10-2017",

year = "2019",

month = jan,

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 proceeding › Conference 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 - https://www.scopus.com/pages/publications/85053249043

UR - https://www.scopus.com/pages/publications/85053249043#tab=citedBy

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

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

M3 - Conference contribution

AN - SCOPUS:85053249043

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

T2 - International Conference on Computer, Communication and Computational Sciences, IC4S 2017

Y2 - 11 October 2017 through 12 October 2017

ER -

Comparative analysis of prediction algorithms for diabetes

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this