Supervised Learning techniques for analysis of neonatal data

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...

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Bibliographic Details
Published in2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) pp. 25 - 31
Main Authors Shirwaikar, Rudresh D., Mago, Nikhit, Acharya U, Dinesh, Makkithaya, Krishnamoorthi, Hegde K, Govardhan
Format Conference Proceeding
LanguageEnglish
Published IEEE 2016
Subjects
Classification
Classification algorithms
Decision trees
Healthcare
Neonate
Pediatrics
Prediction algorithms
Supervised learning
Support vector machines
Training
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Summary: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.
DOI:10.1109/ICATCCT.2016.7911960