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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Validity of predictive equations for determining resting energy expenditure
 Fabián, Patrik ; Korvas, Pavel (referee) ; Chlíbková, Daniela (advisor)
This bachelor thesis deals with the validation of predictive equations to determine resting metabolic rate. The objective of this bachelor thesis was to compare individual predictive equations for determining resting metabolic rate with self-measurement using indirect caloriemetry and to establish a own procedure for predicting resting metabolic rate. The evaluation of each predictive equation was based on statistical analyses. Based on statistical analyses, it was found that the available predictive equations underestimate resting metabolism by an average of 20 % of kilocalories per day compared to the value measured by indirect calorimetry. Machine learning was used to determine the actual procedure for predicting resting metabolic rate, which was then presented using the user interface. The following testing showed that the neural network for predicting resting metabolic rate provides more accurate results compared to the available predictive equations.
Detailed record
Validity of predictive equations for determining resting energy expenditure
 Fabián, Patrik ; Korvas, Pavel (referee) ; Chlíbková, Daniela (advisor)
This bachelor thesis deals with the validation of predictive equations to determine resting metabolic rate. The objective of this bachelor thesis was to compare individual predictive equations for determining resting metabolic rate with self-measurement using indirect caloriemetry and to establish a own procedure for predicting resting metabolic rate. The evaluation of each predictive equation was based on statistical analyses. Based on statistical analyses, it was found that the available predictive equations underestimate resting metabolism by an average of 20 % of kilocalories per day compared to the value measured by indirect calorimetry. Machine learning was used to determine the actual procedure for predicting resting metabolic rate, which was then presented using the user interface. The following testing showed that the neural network for predicting resting metabolic rate provides more accurate results compared to the available predictive equations.
Detailed record

See also: similar author names
1 Fabian, Pavel
7 Fabian, Peter
7 Fabian, Petr
7 Fabián, Petr
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