Classification of infectious and noninfectious diseases using artificial neural networks from 24-hour continuous tympanic temperature data of patients with undifferentiated fever

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four–hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3% for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.

Original languageEnglish
Pages (from-to)173-183
Number of pages11
JournalCritical Reviews in Biomedical Engineering
Volume46
Issue number2
DOIs
Publication statusPublished - 01-01-2018

Fingerprint

Neural networks
Classifiers
Temperature
Learning algorithms
Learning systems
Monitoring

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

@article{0d88dd0b6675492b8c40cb42f48301d7,
title = "Classification of infectious and noninfectious diseases using artificial neural networks from 24-hour continuous tympanic temperature data of patients with undifferentiated fever",
abstract = "Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four–hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3{\%} for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.",
author = "Dakappa, {Pradeepa H.} and Keerthana Prasad and Rao, {Sathish B.} and Ganaraja Bolumbu and Bhat, {Gopalkrishna K.} and Chakrapani Mahabala",
year = "2018",
month = "1",
day = "1",
doi = "10.1615/CritRevBiomedEng.2018025917",
language = "English",
volume = "46",
pages = "173--183",
journal = "Critical Reviews in Biomedical Engineering",
issn = "0278-940X",
publisher = "Begell House Inc.",
number = "2",

}

TY - JOUR

T1 - Classification of infectious and noninfectious diseases using artificial neural networks from 24-hour continuous tympanic temperature data of patients with undifferentiated fever

AU - Dakappa, Pradeepa H.

AU - Prasad, Keerthana

AU - Rao, Sathish B.

AU - Bolumbu, Ganaraja

AU - Bhat, Gopalkrishna K.

AU - Mahabala, Chakrapani

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four–hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3% for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.

AB - Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four–hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3% for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.

UR - http://www.scopus.com/inward/record.url?scp=85055338447&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055338447&partnerID=8YFLogxK

U2 - 10.1615/CritRevBiomedEng.2018025917

DO - 10.1615/CritRevBiomedEng.2018025917

M3 - Article

VL - 46

SP - 173

EP - 183

JO - Critical Reviews in Biomedical Engineering

JF - Critical Reviews in Biomedical Engineering

SN - 0278-940X

IS - 2

ER -