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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 5707162, 6 pages
https://doi.org/10.1155/2017/5707162
Research Article

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

1Department of Internal Medicine, Kasturba Medical College, Manipal University, Mangaluru, Karnataka, India
2School of Information Sciences, Manipal Institute of Technology, Manipal University, Manipal, Karnataka, India
3Department of Physiology, Kasturba Medical College, Manipal University, Mangaluru, Karnataka, India
4Department of Microbiology, Kasturba Medical College, Manipal University, Mangaluru, Karnataka, India

Correspondence should be addressed to Chakrapani Mahabala

Received 15 May 2017; Accepted 31 October 2017; Published 22 November 2017

Academic Editor: Saugat Bhattacharyya

Copyright © 2017 Pradeepa H. Dakappa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 () 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 (, 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.