TY - JOUR
T1 - Classification of fever patterns using a single extracted entropy feature
T2 - A feasibility study based on Sample Entropy
AU - Cuesta-Frau, David
AU - Miró-Martínez, Pau
AU - Oltra-Crespo, Sandra
AU - Molina-Picó, Antonio
AU - Dakappa, Pradeepa H.
AU - Mahabala, Chakrapani
AU - Vargas, Borja
AU - González, Paula
PY - 2019/9/30
Y1 - 2019/9/30
N2 - 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.
AB - 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.
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U2 - 10.3934/mbe.2020013
DO - 10.3934/mbe.2020013
M3 - Article
C2 - 31731349
AN - SCOPUS:85075037881
SN - 1547-1063
VL - 17
SP - 235
EP - 249
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
IS - 1
ER -
