Open AccessClassification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy
Author(s) -
David Cuesta–Frau,
Pau Miró–Martínez,
Sandra Oltra–Crespo,
Antonio Molina Picó,
Pradeepa H. Dakappa,
Chakrapani Mahabala,
Borja Vargas,
Paula González
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020013
Subject(s) - pattern recognition (psychology) , entropy (arrow of time) , artificial intelligence , sample entropy , computer science , tuberculosis , feature (linguistics) , dengue fever , data mining , mathematics , medicine , pathology , physics , linguistics , philosophy , quantum mechanics
Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied.