Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests

Nikhit Mago; Shikhar Srivastava; Rudresh D. Shirwaikar; U. Dinesh Acharya; Leslie Edward S. Lewis; M. Shivakumar

doi:10.1109/IC3I.2016.7918051

Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests

Nikhit Mago, Shikhar Srivastava, Rudresh D. Shirwaikar, U. Dinesh Acharya, Leslie Edward S. Lewis, M. Shivakumar

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

7 Citations (Scopus)

Abstract

Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's birth using data collected during the first three days of neonatal life. This paper also uses an optimization method called Synthesized Minority Oversampling Technique (SMOTE) to resolve the class imbalance problem observed in the data. Principal Component Analysis and one-hot encoding have been implemented for feature extraction and data preprocessing respectively. Among the results obtained, an AUC of 0.72 using the amalgamation of Random Forests and SMOTE is found to be the most accurate model.

Original language	English
Title of host publication	Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016
Editors	V N Manjunatha Aradhya, S K Niranjan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	693-697
Number of pages	5
ISBN (Electronic)	9781509052554
DOIs	https://doi.org/10.1109/IC3I.2016.7918051
Publication status	Published - 01-01-2016
Event	2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 - Noida, India Duration: 14-12-2016 → 17-12-2016

Publication series

Name	Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016

Conference

Conference	2nd International Conference on Contemporary Computing and Informatics, IC3I 2016
Country/Territory	India
City	Noida
Period	14-12-16 → 17-12-16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems and Management
Health Informatics
Computer Networks and Communications
Computer Science Applications
Information Systems

Access to Document

10.1109/IC3I.2016.7918051

Cite this

Mago, N., Srivastava, S., Shirwaikar, R. D., Acharya, U. D., Lewis, L. E. S., & Shivakumar, M. (2016). Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests. In V. N. M. Aradhya, & S. K. Niranjan (Eds.), Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 (pp. 693-697). Article 7918051 (Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC3I.2016.7918051

Mago, Nikhit ; Srivastava, Shikhar ; Shirwaikar, Rudresh D. et al. / Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016. editor / V N Manjunatha Aradhya ; S K Niranjan. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 693-697 (Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016).

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title = "Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests",

abstract = "Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's birth using data collected during the first three days of neonatal life. This paper also uses an optimization method called Synthesized Minority Oversampling Technique (SMOTE) to resolve the class imbalance problem observed in the data. Principal Component Analysis and one-hot encoding have been implemented for feature extraction and data preprocessing respectively. Among the results obtained, an AUC of 0.72 using the amalgamation of Random Forests and SMOTE is found to be the most accurate model.",

author = "Nikhit Mago and Shikhar Srivastava and Shirwaikar, \{Rudresh D.\} and Acharya, \{U. Dinesh\} and Lewis, \{Leslie Edward S.\} and M. Shivakumar",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 ; Conference date: 14-12-2016 Through 17-12-2016",

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doi = "10.1109/IC3I.2016.7918051",

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pages = "693--697",

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Mago, N, Srivastava, S, Shirwaikar, RD, Acharya, UD , Lewis, LES & Shivakumar, M 2016, Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests. in VNM Aradhya & SK Niranjan (eds), Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016., 7918051, Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, Institute of Electrical and Electronics Engineers Inc., pp. 693-697, 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, Noida, India, 14-12-16. https://doi.org/10.1109/IC3I.2016.7918051

Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests. / Mago, Nikhit; Srivastava, Shikhar; Shirwaikar, Rudresh D. et al.
Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016. ed. / V N Manjunatha Aradhya; S K Niranjan. Institute of Electrical and Electronics Engineers Inc., 2016. p. 693-697 7918051 (Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016).

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

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AU - Srivastava, Shikhar

AU - Shirwaikar, Rudresh D.

AU - Acharya, U. Dinesh

AU - Lewis, Leslie Edward S.

AU - Shivakumar, M.

PY - 2016/1/1

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Mago N, Srivastava S, Shirwaikar RD, Acharya UD , Lewis LES, Shivakumar M. Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests. In Aradhya VNM, Niranjan SK, editors, Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 693-697. 7918051. (Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016). doi: 10.1109/IC3I.2016.7918051

Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests

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