Supervised Learning techniques for analysis of neonatal data

Rudresh D. Shirwaikar, Nikhit Mago, U. Dinesh Acharya, Krishnamoorthi Makkithaya, K. Govardhan Hegde

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

3 Citations (Scopus)

Abstract

In the current healthcare setup, the use of Machine Learning has been limited as clinicians diagnose and administer treatment manually. Supervised Learning techniques can help solve many prognostic problems and help clinicians in taking decisions pertaining to healthcare. The paper presents selected machine learning techniques that can be applied for medical data, and in particular some supervised learning techniques with their applications on the analysis of neonatal data. The goal of the paper is to review and discuss the methodology, advantages and disadvantages of supervised learning techniques and the use on neonatal data. In addition, this paper also highlights the model evaluation parameters and also suggests the ways to improve the performance of a model designed for neonatal data analysis.

Original languageEnglish
Title of host publicationProceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-31
Number of pages7
ISBN (Electronic)9781509023981
DOIs
Publication statusPublished - 25-04-2017
Event2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016 - Bengaluru, Karnataka, India
Duration: 21-07-201623-07-2016

Conference

Conference2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016
CountryIndia
CityBengaluru, Karnataka
Period21-07-1623-07-16

Fingerprint

Supervised learning
Learning systems

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Software

Cite this

Shirwaikar, R. D., Mago, N., Dinesh Acharya, U., Makkithaya, K., & Govardhan Hegde, K. (2017). Supervised Learning techniques for analysis of neonatal data. In Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016 (pp. 25-31). [7911960] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICATCCT.2016.7911960
Shirwaikar, Rudresh D. ; Mago, Nikhit ; Dinesh Acharya, U. ; Makkithaya, Krishnamoorthi ; Govardhan Hegde, K. / Supervised Learning techniques for analysis of neonatal data. Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 25-31
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Shirwaikar, RD, Mago, N, Dinesh Acharya, U, Makkithaya, K & Govardhan Hegde, K 2017, Supervised Learning techniques for analysis of neonatal data. in Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016., 7911960, Institute of Electrical and Electronics Engineers Inc., pp. 25-31, 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016, Bengaluru, Karnataka, India, 21-07-16. https://doi.org/10.1109/ICATCCT.2016.7911960

Supervised Learning techniques for analysis of neonatal data. / Shirwaikar, Rudresh D.; Mago, Nikhit; Dinesh Acharya, U.; Makkithaya, Krishnamoorthi; Govardhan Hegde, K.

Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 25-31 7911960.

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

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Shirwaikar RD, Mago N, Dinesh Acharya U, Makkithaya K, Govardhan Hegde K. Supervised Learning techniques for analysis of neonatal data. In Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 25-31. 7911960 https://doi.org/10.1109/ICATCCT.2016.7911960