Predicting effect of physical factors on tibial motion using artificial neural networks

Authors:
Murat Sari;B. Gultekin Cetiner
Affiliations:
Department of Mathematics, Faculty of Art and Science, Pamukkale University, 20070 Denizli, Turkey;Faculty of Engineering, Department of Manufacturing Engineering, IIUM, Jalan Gombak, 53100 Kuala Lumpur, Malaysia
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 0
Cited 2

Dengue confirmed-cases prediction: A neural network model

Expert Systems with Applications: An International Journal
Improved Zhang neural network model and its solution of time-varying generalized linear matrix equations

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

The aim of this study was to predict the effect of physical factors on tibial motion by making use of artificial neural networks (ANNs). Since assessment of the tibial motion by the conventional approaches is generally difficult, this study aimed at the prediction of the relations between several physical factors (gender, age, body mass, and height) and tibial motion in terms of the ANNs. Data collected for 484 healthy subjects have been analyzed by using the ANNs. The study has given encouraging results for such a purpose. This investigation has been made to predict the rotations; especially the RTER prediction is highly satisfactory and the ANNs have been found to be very promising processing systems for modelling in the tibial rotation data assessments.