Supervised Learning techniques for analysis of neonatal data

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

doi:10.1109/ICATCCT.2016.7911960

Supervised Learning techniques for analysis of neonatal data

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

School of Computer Engineering

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

7 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 language	English
Title of host publication	Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016
Editors	S.K. Niranjan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	25-31
Number of pages	7
ISBN (Electronic)	9781509023981
DOIs	https://doi.org/10.1109/ICATCCT.2016.7911960
Publication status	Published - 25-04-2017
Event	2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016 - Bengaluru, Karnataka, India Duration: 21-07-2016 → 23-07-2016

Publication series

Name	Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016

Conference

Conference	2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016
Country/Territory	India
City	Bengaluru, Karnataka
Period	21-07-16 → 23-07-16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Signal Processing
Software

Access to Document

10.1109/ICATCCT.2016.7911960

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 S. K. Niranjan (Ed.), Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016 (pp. 25-31). Article 7911960 (Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICATCCT.2016.7911960

Shirwaikar, Rudresh D. ; Mago, Nikhit ; Dinesh Acharya, U. et al. / 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. editor / S.K. Niranjan. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 25-31 (Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016).

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title = "Supervised Learning techniques for analysis of neonatal data",

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.",

author = "Shirwaikar, \{Rudresh D.\} and Nikhit Mago and \{Dinesh Acharya\}, U. and Krishnamoorthi Makkithaya and \{Govardhan Hegde\}, K.",

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language = "English",

series = "Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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 SK Niranjan (ed.), Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016., 7911960, Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016, 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. et al.
Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016. ed. / S.K. Niranjan. Institute of Electrical and Electronics Engineers Inc., 2017. p. 25-31 7911960 (Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016).

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

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AU - Shirwaikar, Rudresh D.

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AU - Makkithaya, Krishnamoorthi

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PY - 2017/4/25

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N2 - 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.

AB - 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.

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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 Niranjan SK, editor, 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. (Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016). doi: 10.1109/ICATCCT.2016.7911960

Supervised Learning techniques for analysis of neonatal data

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