Supervised Learning Techniques for Analysis of Neonatal Data

Hegde, Govardhan and Makkithaya, Krishnamoorthi and Acharya, Dinesh U and Mago, Nikhit and Shirwaikar, Rudresh D (2017) Supervised Learning Techniques for Analysis of Neonatal Data. In: 2nd international Conference on Applied and Theoretical computing and communication Technology, 21/07/2016, SJB Institute of Technology, Bangalore.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Supervised Learning; Classification; Neonate; Healthcare
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 27 Jul 2016 15:21
Last Modified: 28 Dec 2017 10:42

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