Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Suresh, Shruthi; Newton, David T.; Everett, Thomas H.; Lin, Guang; Duerstock, Bradley S.

doi:10.3389/fninf.2022.901428

Title: Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Abstract

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

Authors:

Suresh, Shruthi ^[1]; Newton, David T. ^[1]; Everett, Thomas H. ^[2]; Lin, Guang ^[1]; Duerstock, Bradley S. ^[1]

Purdue University, West Lafayette, IN (United States)
Indiana University School of Medicine, Indianapolis IN (United States)

Publication Date:: Wed Aug 10 00:00:00 EDT 2022

Research Org.:: Purdue Univ., West Lafayette, IN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Department of Defense (DOD); Indiana State Department of Health; National Institutes of Health (NIH); Krannert Institute of Cardiology; National Science Foundation (NSF); Army Research Office (ARO)

OSTI Identifier:: 1981089

Grant/Contract Number:: SC0021142; SC190164; R42DA043391; OT2OD028183-01; DMS-1555072; DMS-1736364; CMMI-1634832; CMMI-1560834; 382247; W911NF-15-1-0562

Resource Type:: Accepted Manuscript

Journal Name:: Frontiers in Neuroinformatics

Additional Journal Information:: Journal Volume: 16; Journal ID: ISSN 1662-5196

Publisher:: Frontiers Media S.A.

Country of Publication:: United States

Language:: English

Subject:: spinal cord injuries; machine learning; feature selection; electrocardiography; healthcare

Citation Formats


                    Suresh, Shruthi, Newton, David T., Everett, Thomas H., Lin, Guang, and Duerstock, Bradley S. Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.  United States: N. p., 2022. 
Web.  doi:10.3389/fninf.2022.901428.

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                    Suresh, Shruthi, Newton, David T., Everett, Thomas H., Lin, Guang, & Duerstock, Bradley S. Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.  United States.  https://doi.org/10.3389/fninf.2022.901428

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                    Suresh, Shruthi, Newton, David T., Everett, Thomas H., Lin, Guang, and Duerstock, Bradley S. Wed .  
"Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia".  United States.  https://doi.org/10.3389/fninf.2022.901428.  https://www.osti.gov/servlets/purl/1981089.

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@article{osti_1981089,

  title        = {Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia},

  author       = {Suresh, Shruthi and Newton, David T. and Everett, Thomas H. and Lin, Guang and Duerstock, Bradley S.},

  abstractNote = {Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.},

  doi          = {10.3389/fninf.2022.901428},

  journal      = {Frontiers in Neuroinformatics},

  number       = ,

  volume       = 16,

  place        = {United States},

  year         = {Wed Aug 10 00:00:00 EDT 2022},

  month        = {Wed Aug 10 00:00:00 EDT 2022}

}

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Similar Records

Title: Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Abstract

Citation Formats

Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants journal, September 2010

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Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants
journal, September 2010

Automated feature selection for obstructive sleep apnea syndrome diagnosis
journal, January 2020

Applied Predictive Modeling
book, January 2013

An Introduction to Machine Learning
journal, March 2020

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journal, April 2020

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journal, February 2019

Application of classification techniques on development an early-warning system for chronic illnesses
journal, August 2012

Using skin sympathetic nerve activity to estimate stellate ganglion nerve activity in dogs
journal, June 2015

Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
journal, August 2017

Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine
journal, March 2007

Recording sympathetic nerve activity from the skin
journal, October 2017

Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques
journal, October 2016

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journal, April 2020

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