A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording

Dakappa, Pradeepa H. and *, Keerthana Prasad and Rao, Satish B and *, Ganaraja B and Bhat, Gopalkrishna K and *, Chakrapani M (2017) A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording. Journal of Healthcare Engineering, 2017 (570716). pp. 1-6. ISSN 20402295

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Abstract

Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n = 96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p < 0 001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.

Item Type: Article
Uncontrolled Keywords: Temperature,Tuberculosis, Machine learning, continuous
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Medicine > KMC Mangalore > Microbiology
Medicine > KMC Mangalore > Physiology
Medicine > KMC Mangalore > Medicine
Depositing User: KMCMLR User
Date Deposited: 29 Nov 2017 09:46
Last Modified: 29 Nov 2017 09:46
URI: http://eprints.manipal.edu/id/eprint/150100

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