Using the Gamma Test in the Analysis of Classification Models for Time-Series Events in Urodynamics Investigations
Urodynamics is a clinical test in which time series data is recorded measuring internal pressure readings as the bladder is filled and emptied. Two sets of descriptive statistics based on various pressure events from urodynamics tests have been derived from time series data. The suitability of these statistics for use as inputs for event classification through neural networks is investigated by means of the gamma test. BFGS neural network models are constructed and their classification accuracy measured. Through a comparison of the results, it is shown that the gamma test can be used to predict the reliability of models before the neural network training phase begins.
KeywordsHide Layer Mean Square Error Classification Accuracy Classification Model Pressure Event
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